JADH 2017
Proceedings of the 7th Conference of Japanese
Association for Digital Humanities“Creating Data through Collaboration”

Japanese
Association for
DigitalHumanities

JADH2017
Proceedings of the 7th Conference ofJapanese Association for Digital Humanities“Creating Data through Collaboration”
http://conf2017.jadh.org/
Doshisha University, September 11-12, 2017

Hosted by:

JADH2017 Organizing Committee
under the auspices of the Japanese Association for Digital Humanities
Co-hosted by:

Department of Culture and Information Science, Doshisha University
Supported by:Construction of a New Knowledge Base for Buddhist Studies:
Presentation of an Advanced Model for the Next Generation of Humanities Research(15H05725, Masahiro Shimoda)
International Institute for Digital Humanities
Co-sponsored by:

PSJ SIG Computers and the Humanities
Japan Society for Digital Archive
Japanese Society for Information and Media Studies
Japan Art Documentation Society (JADS)
Japan Association for East Asian Text Processing (JAET)
Japan Association for English Corpus Studies
The Mathematical Linguistic Society of Japan
Japan Society of Information and Knowledge
Alliance of Digital Humanities Organizations

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Table of Contents
JADH 2017 Organization ........................................................................................... vi
Time Table ...................................................................................................................... vii
Keynote lecture・Collaboration at scale: emerging infrastructures for digital scholarship ...... viii

Special thematic session
・The Power of Crowdsourcing and Libraries ............................................................ ix

Panel session:・Transformative Data: Data-Driven Reconfigurations of Interdisciplinary
Work in the Digital Humanities ....................................................................................... x

Session 1:
・(S1-01; Long) Macroscopic Exploration of Large Text and Image Collections
via Similarity Heatmaps ....................................................................................................1・(S1-02; Long) A Neural Network Approach to Historic Linguistic Change...................................................................................................................................................5・(S1-03; Long) Vector based measure of semantic shifts across different
cultural corpora as a proxy to comparative history of ideas .......................... 12

Session 2:

ii

・(S2-01; Long) FAIRness for Citizens: Workflow and Platform for Open Datawith a Case Study on Edo Cooking Recipes ........................................................... 14

・(S2-02; Short) FloraCultures: Conserving Plant-Based Cultural Heritage inAustralia through Digital Approaches ...................................................................... 17・(S2-03; Short) Making a Mark: Tradition and Technology in the Digitizationof the Japanese Votive Slips Collection at the University of Oregon ........... 19

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Session 3:
・(S3-01; Long) Language Documentation and Standards in Digital
Humanities: TEI and the documentation of Mixtepec-Mixtec .......................... 21

・(S3-02; Short) Application of the Concept of “Linkbase” for Digitalizationof Linguistic Resources and Analysis ....................................................................... 24

・(S3-03; Short) TEI/XML Markup of Engi-shiki as Research Platform for
Historians of Ancient Japan ......................................................................................... 26

・(S3-04; Short) Ontological Engineering in Philosophy. Example ofPhenomenological Data ................................................................................................. 29

Poster session:・(P01) Development of a title authority database for Chinese classic works................................................................................................................................................ 31

・(P02) Universal Dependency for Modern Japanese ........................................... 34・(P03) CBETA Research Platform: A Digital Research Environment forStudying Chinese Buddhist Literature in the New Era ....................................... 37・(P04) Situational Effects on Functional Word Frequencies within
Conversational Sentences in Japanese Novels ..................................................... 40・(P05) Enabling digital humanities research and teaching through digital
library APIs ......................................................................................................................... 43・(P06) Surviving the storm: The Revival of Edo Cultural Traditions throughLocal and Cultural Networking .................................................................................... 45・(P07) Towards a Conceptual Framework for Superworks ............................... 47

・(P08) Prototype of Linked Open Data Model for Tang Poems........................ 50

・(P09) Extracting the Patterns of Harmonic Features within MozartʼsSymphonies and String Quartets Compositions based on the Notion of
Pitch-Class Set .................................................................................................................. 53・(P10) Creating A Work Entity Dataset of Console Games Using Wikipedia .... 56

・(P11) Visualizing Narratives of the Atomic Bombings of Hiroshima andiii

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Nagasaki .............................................................................................................................. 58
・(P12) Deep Features for Image Classification and Image Similarity
Perception .......................................................................................................................... 60・(P13) Constructing a Comprehensive Online Platform for Corpus Studies in
Taiwan................................................................................................................................... 63・(P14) Counting the Traditional Japanese Musical Scales: Analyzing Folk
Songs from Chugoku and Kyushu Districts ........................................................... 65

・(P15) Development of System to Encourage Data Sharing and Usage
among Process on Historical Study with Linked Data........................................ 68

・(P16) Relationships between Flowers in a Word Embedding Space ofClassic Japanese Poetry................................................................................................ 70㹒Session 4:
・(S4-01; Long) Mapping Dickensʼs Novels in a Network of Words, Topics,
and Texts: Topic Modelling a Corpus of Classic Fiction ..................................... 73

・(S4-02; Long) Analyzing Features for the Detection of Happy Endings inGerman Novels .................................................................................................................. 79

・(S4-03; Long) Machine-Learning Approaches to Literary Works: Novels of
Sir Arthur Conan Doyle .................................................................................................. 84

Session 5:
・(S5-01; Long) Human-assisted OCR of Japanese Books with Multistep
Microtasks .......................................................................................................................... 88・(S5-02; Long) Creating Geotagged Humanities Data via Mobile Phone:
Opportunities and Challenges ..................................................................................... 89

・(S5-03; Long) Another Kind of Mime. Another Kind of Digital Humanities ... 92

Session 6:
・ (S6-01; Long) Matrix and Graph Operations for Relationship Inference: An
Illustration with the Kinship Inference in the China Biographical Database . 94

・iv

(S6-02; Short) Semi-automatic reconstruction of category for integrated
ordinance database......................................................................................................... 97

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・

(S6-03; Short) Employing Syntactic Features in Authorship Attribution ofThree Writers: Alice Bradley Sheldon, Ernest Hemingway, and Theodore
Sturgeon ............................................................................................................................100

・(S6-04; Short) Annotation of ʻWord List by Semantic Principlesʼ Labelsfrom the ʻCorpus of Historical Japaneseʼ Heian Period Series− Trial Annotation on Tosa Nikki and Taketori Monogatari − ......................104

・(S6-05; Short) Building Networks and Creating Access in Early Modern
Europe ................................................................................................................................107v

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JADH 2017 Organization
JADH 2017 Organizing Committee:
・Takashi Harada(Doshisha University)
・Sho Sato (Doshisha University)
・Akihiro Kawase (Doshisha University), Chair
・Gen Tsuchiyama (Doshisha University)
・Kiyonori Nagasaki (International Institute for Digital Humanities, Japan)
・Toru Tomabechi (International Institute for Digital Humanities, Japan)

JADH 2017 Program Committee:
・Paul Arthur (Edith Cowan University, Australia)
・James Cummings (Newcastle University, UK)
・J. Stephen Downie (University of Illinois, USA)・Øyvind Eide (University of Cologne and University of Passau, Germany)
・Neil Fraistat (University of Maryland, USA)・Makoto Goto (National Museum of Japanese History, Japan)
・Shoichiro Hara (Kyoto University, Japan)
・Jieh Hsiang (National Taiwan University, Taiwan)
・Akihiro Kawase (Doshisha University, Japan)
・Asanobu Kitamoto (National Institute of Informatics, Japan), Chair
・Maciej Eder (Pedagogical University of Kraków, Poland)・Charles Muller (University of Tokyo, Japan)
・Hajime Murai (Tokyo Institute of Technology, Japan)
・Kiyonori Nagasaki (International Institute for Digital Humanities, Japan)
・John Nerbonne (University of Groningen, Netherlands)
・Geoffrey Rockwell (University of Alberta, Canada)・Susan Schreibman (National University of Ireland Maynooth, Ireland)
・Masahiro Shimoda (University of Tokyo, Japan)
・Raymond Siemens (University of Victoria, Canada)・Keiko Suzuki (Ritsumeikan University, Japan)
・Takafumi Suzuki (Toyo University, Japan)
・Tomoji Tabata (Osaka University, Japan)
・Toru Tomabechi (International Institute for Digital Humanities, Japan)
・Kathryn Tomasek (Wheaton College, USA)・Christian Wittern (Kyoto University, Japan)
・Taizo Yamada (University of Tokyo, Japan)vi

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Time Table
September 11, Day 1
08:30
Registration
09:00-09:15 Opening
09:15-10:45 Session 1
10:45-11:00 Break
11:00-12:15 Session 2
12:15-13:30 Lunch Break
13:30-14:45 Session 3
14:45-15:00 Break
15:00-16:10 Panel session
16:10-16:25 Break
16:25-17:25 Keynote Lecture
17:30-18:30 Poster Slam and Core Time
19:00Reception

September 12, Day 2
09:00-10:30 Session 4
10:30-10:45 Break
10:45-12:15 Session 5
12:15-13:30 Lunch break (Annual General Meeting of JADH)
13:30-14:50 Special thematic session
14:50-15:05 Break
15:05-16:35 Session 6
16:35-17:00 Closing

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[Keynote lecture]
Collaboration at scale: emerging infrastructures for
digital scholarship
Donald Sturgeon
Harvard University
Modern technological society is possible only as a result of collaborations constantly taking
place between countless individuals and groups working on tasks which at first glance may
seem independent from one another yet are ultimately connected through complex
interdependencies. Just as technological progress is not merely a story of ever moresophisticated technologies, but also of the evolution of increasingly efficient structures
facilitating their development, so too scholarship moves forward not just by the creation of evermore nuanced ideas and theories, but also by increasingly powerful means of identifying,exchanging, and building upon these ideas.
The digital medium presents revolutionary opportunities for facilitating such tasks in humanities
scholarship. Most obviously, it offers the ability to perform certain types of analyses on scales
larger than would ever have been practical without use of computational methods ‒ for example
the examination of trends in word usage across millions of books, or visualizations of social
interactions of tens of thousands of historical individuals. But it also presents opportunities forvastly more scalable methods of collaboration between individuals and groups working on
distinct yet related projects. Simple examples are readily available: computer scientists develop
and publish code through open source platforms, companies further adapt it for use incommercial systems, and humanities scholars to apply it to their own research; libraries digitize
and share historical works from their collections, which are transcribed by volunteers, searched
and read by researchers and cited in scholarly works.
Much of the infrastructure already in use in digital scholarship is infrastructure developed formore general-purpose use ‒ a natural and desirable development given the obvious economies
of scale which result from this. However, as the application of digital methods in humanities
scholarship becomes increasingly mainstream, as digitized objects of study more numerous, andrelated digital techniques more specialized, the value of infrastructure designed specifically tosupport scholarship in particular fields of study becomes increasingly apparent. This paper will
examine types of humanities infrastructure projects which are emerging, and the potential they
have to facilitate scalable collaboration within and beyond distributed scholarly communities.

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[Special thematic session]
The Power of Crowdsourcing and Libraries
Azusa Tanaka (University of Washington),
Takashi Harada (Doshisha University),
Kiyonori Nagasaki (International Institute for Digital Humanities),
Kosetsu Ikeda (Chiba University),
Atsuyuki Morishima (University of Tsukuba)
Abstract
Crowdsourcing is recognized as a key technology having a big impact on library and digital
humanity domains. It has already been used for many purposes such as transcribing texts,
creating metadata, and correcting errors in bibliographic records. This session has one keynote
and several reports that address ongoing crowdsourcing projects in/for libraries in the UnitedStates and Japan and how crowds can help us solve a variety of problems. The talks also
discuss important issues found in those projects, potentials and future directions ofcrowdsourcing and libraries.

Biography
Azusa Tanaka (University of Washington)
Azusa Tanaka is a Japanese Studies Librarian at University of Washington and handles subject
liaison, reference, instruction, collection development, and cataloging. More about her research:https://jisao-washington.academia.edu/AzusaTanaka.
Takashi Harada (Doshisha University)
Takashi Harada is a professor at the Center for License and Qualifications in Doshisha
University. Aside from the work in the center, he is responsible for the education in the Library
and Information Science masterʼ s course as a professor of the Graduate School of Policy andManagement in Doshisha University. His research interests include the design and analysis ofintegrated library systems and social information systems. He is president of Project Next-L, alibrary community initiative to develop specifications for the open source next generation
integrated library system.
Kiyonori Nagasaki (International Institute for Digital Humanities)
Kiyonori Nagasaki is a Senior Fellow at the International Institute for Digital Humanities in Tokyo.
His main research interest is in the development of digital frameworks for collaboration in
Buddhist studies. He is also engaging in investigation into the significance of digital methodology
in Humanities and in promotion of DH activities in Japan.
Kosetsu Ikeda (Chiba University)
Kosetsu Ikeda is an Assistant Professor at Chiba University. His current main research interests
include crowdsourcing, library and information science, graph data theory, and data engineering.
Atsuyuki Morishima (University of Tsukuba)
Atsuyuki Morishima is a Professor of University of Tsukuba, Japan. His research interests
include data-centric human-machine computations, data integration, and Web data management.
Currently, he is the chair of SIG-DBS of the Information Processing Society of Japan, an editorin-chief of IPSJ transactions on Database Systems, and an associate editor of the VLDB Journal.

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[Panel session]
Transformative Data: Data-Driven Reconfigurations ofInterdisciplinary Work in the Digital Humanities
Kristin Allukian (University of South Florida),
Mauro Carassai (California State University, Northridge),
Laura Hale (ParaSport News)
Abstract
Spanning three particular historical moments over the last century and foregrounding different
DH approaches to womenʼs visual history, literary theory, and disability sports studies, ourinterdisciplinary panel addresses the relationship between data collection and gender,
subjectivity, and ableism in our DH research, teaching, and across the ever-shifting meaning of“data” within the current political and academic climate. Our papers individually and collectively
address the conference theme of crossing borders in digital humanities work by using data
analysis as a practice that connects different branches of knowledge. Kristin Allukianʼs project,which involves the collaboration of undergraduate students, graduate students, and faculty, canbe seen as interdisciplinary in nature, crossing various academic fields including literature,
womenʼs studies, visual history, and politics. Similarly, Mauro Carassaiʼs paper develops on thethreshold between linguistic, literature, and anthropology by looking at the depiction of thehuman, machinic, and cybernetic subject when the set of unstructured set of data is constitutedby an artificially constructed language. Finally, Laura Haleʼs paper examines how organizations
do and do not cross borders when accessing data on disability sports for either commercial or
non-profit purposes inviting a general scholarly reflection that involves social sciences, business,
and knowledge management. Looking at a series of case studies at different moments in timeand across different academic fields, our panels aims to strengthen an understanding of how
data-driven inquiries might mobilize past and present dissent, ambiguous borders of intercultural linguistic domains, and information accessibility in the research, teaching, andparticipation of the digital humanities.
Kristin Allukian begins the panel in the early twentieth century, arguing feminist digital
humanities practices of archiving pro- and anti-suffrage postcards can engender new visual
historical narratives of masculinity, manhood, and fatherhood. Much work has been done on
visual representations of women in suffrage postcards; broadly speaking, scholars discuss how
the strictures of white heteronormative femininity and the expectations of motherhood were
important tropes in suffrage imagery. Much less, however, has been said about how depictionsof masculinity and fatherhood operated in suffrage postcards. This project, the Suffrage
Postcard Project, utilizes a range of digital tools including Omeka, ImagePlot, Gephi, Tableau
Public, and Iconclass to explore how narratives of masculinity and fatherhood contributed to thesuffrage debate and fictional literature of the time.
Moving from the visual image to the written page, Mauro Carassai extends the inquiry on
representation to the depiction of the human, machinic, and cybernetic subject when the set of
unstructured set of data is constituted by an artificially constructed language. Computerenhanced methodologies of textual analysis can reveal their limits, as well as their advantages,
when the encoded semi-structured data undergoing data mining practices are constructed inways that resist digital discrete differentiation as the creolized Caribbean linguistic set in Nalo
Hopkinsonʼs novel Midnight Robber does. Carassaiʼs paper offers a set of different of “distantreadings” of the novel operated by means of various software (Voyant, Lexos, NLT) andcompares the setʼs results with textual data parsed by means of stylometric software (Signature,
stylo, of Stylene) in order to raise problems in specific theories of marginalized subjectivity thatmight (and should) influence the future direction of software development for textual literary
analysis.
Moving from a forum that is literary is nature to one that is athletic in nature, Laura Haleʼs
project explores the practical issues of creating a large-scale database for disability sport, byx

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working to create a database of disability sports information. The primary focus is on discussing
data accessibility, the decision-making process for the software used, questions around how
accessible data should be, and balancing the desire for openness to improve the lives of people
with disabilities and improving their sporting performance with trying to monetize these efforts
to enable the project to continue. Hale argues that using large datasets, including publicly
available datasets that describe general disability characteristics against sporting performancebased on classification and gender, can provide greater insight as opposed to the use of smallerscale data analysis that tends to be based around single events.
These three case studies use a wide range of digital tools to remap sites of dissent, identity
formation and identity politics, and data accessibility across time and disciplines, providing
additional critical networks of gender, cultural, and disability studies ̶networks that are still in
the process of being constructed by means of current digital humanities practices andmethodologies.

Individual Papers
1. Kristin Allukian, “The Illustration, the Image, and the Archive: Feminist Digital Humanities
Approaches to Caricatures of Masculinity in American Suffrage Postcards, 1900-1920”
In 2017, we have Twitter, Instagram, Snapchat, and Facebook. A hundred years earlier, there
were postcards. In the “Golden Age” of postcards (1902-1915), postcards circulated with thesame fervor, albeit not speed, of images on popular social media apps today. This project looks
back at the early decades of the 1900s in the context of the womenʼs suffrage movement, amovement that was gaining momentum in the same historical moment of the Golden Age of
postcards. The Suffrage Postcard Project utilizes a range of digital tools including Omeka,
ImagePlot, Gephi, Tableau Public, and Iconclass to explore two overarching questions: 1. how canfeminist digital humanities practices engender new visual historical narratives of masculinity,
manhood, and fatherhood? And 2. how does this lend an understanding of how such narrativesof masculinity and fatherhood contributed to the suffrage debate?
A team of undergraduate students, graduate students, and faculty collaborate to upload andtag postcard images using an approach that has been influenced by Jacqueline Wernimont andJulia Flandersʼ 2010 article “Feminism in the Age of Digital Archives.” In relation to building theirdigital archive of early modern womenʼs writing, Wernimont and Flanders describe specificchallenges by explaining that “[womenʼs] writing often confounds the processes ofcategorization, explication, and description central to digital text markup…. The work ofdigitization and encoding also engages us in a reflexive process that forces us to interrogate
those genres and any genre-tags that we may use in creating the textbase” (427-429). For our
project, some of the comparable issues we grappled with include tagging “space” and “satire.”
More specifically, the kitchen or house becomes a domestic space that is turned upside downby the fatherʼs presence and/or the motherʼs absence therein: what is the best phrase tocapture this phenomenon? And the use of satire, specifically relating to men dressed inwomenʼs clothes are, at the surface level, merely cross-dressing; yet, whether or not the artist
and/or publisher intended, these images also evoked transgressive ideas about parenting
regardless of which gender is performing the work. Our team collaborates on definitions of tagswith the understanding that such definitions directly influence the search results returned by
the user of our archive and on the exportation of Omeka metadata to other digital tools. The
preliminary results from this digital humanities approach have revealed the degree to which
representations of fatherhood and masculinity were central to the construction of both the proand anti-suffrage debate. Though Susan B. Anthonyʼs politicking resulted in her memorializationin written and visual culture as the premier suffragist of the nineteenth century, a DH approachto the analysis of fatherhood in pro- and anti-suffrage postcards reveals that an additionalversion of visual history is being told.
We propose that the locus for images of ineffective and bewildered fathering in popular culture
can be found in these anti-suffrage postcards, creating a trope that continued in twentiethcentury popular culture. The film Mary Poppins (1964), for example, with its ridicule of thewoman suffragist, presents a family that is ineffective because both parents are inattentive totheir children. Similarly, Three Men and a Baby (1987), Cheaper by the Dozen (2003), andxi

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countless television commercials for baby products satirize clueless fathers; in Mrs. Doubtfire(1993) the father character must dress as a woman in order to be a “good father.” Since
demonizing feminized fatherhood or valorizing national fatherhood had little immediate use forreformers once suffrage was achieved, we tentatively suggest that there was, in addition to thetrope of the ineffective father, a second shift. For ex-suffragists in the 1920s and 1930s, there
was a shift toward claiming the foremothers of womenʼs suffrage across various visual forms.
As postcards waned in popularity, the now-respectable victory of womenʼs suffrage wasmemorialized in various forms. Photographs, sketches, and sculptures of reform women
including Lucretia Mott, Abigail Adams, and of course Anthony herself abounded ‒ proclaimingthat Abraham Lincoln no longer need be the only father of the nation, but that women could
equally be noble mothers of the nation.
Omeka: https://thesuffragepostcardproject.omeka.net/about; Twitter: @Suff̲Postcards
2. Mauro Carassai, “Future directions in distant reading the subject: strategies and challenges in
the computer-enhanced analysis of hybrid languages in Caribbean fiction”
In my paper I discuss the challenges hybrid languages can pose to the computer-enhanced
textual analysis of topics such as the theme of human and machinic subjectivity in literary
representations. As a work of speculative fiction that probes the limits of disciplinary divisions
between digital studies and (New) American Studies, Nalo Hopkinsonʼs Midnight Robber reinscribes the universalizing post-humanist paradigm usually associated with technological culture
within a linguistic creolization process that invests various sentient creatures (humans andmachines). In the futuristic world described in the novel, Hopkinson creates a new language, aversion of pidgin English that includes elements of Jamaican, Trinidadian, and Guyanese. One of
the interesting aspects of this creolized English is that, from the grammar point of view,
subjective pronouns indicating the direct object (i.e. the element of the sentence that receives
the action performed by the subject) are frequently morphologically reconfigured in thesubjective form. In other words, both for humans and machinic characters the English word “me”
regularly appears as “I” and the word “us” regularly appears as “we” regardless of their grammar
function as either subject or direct object.
Different textual analysis software (Voyant, Lexos, NLT) can help in different ways in the analysisof the theme of subjectivity by offering differing strategies to operate distinctions among thevarious pronouns. Some software can operate in terms of both “concomitant variations” and“cluster analysis” patterns when the goal is to differentiate human characters from machinic
characters. However, things become more complicated when the goal is to analyze the different
human and machinic “constructs” that Hopkinson builds in the novel. The fictional digital entities
created by the author represent an array of intermediate cases both in terms of blending thehuman and the machinic and in terms of exhibiting properties of “relational artifacts” in Sherry
Turkleʼs terms. As digital humanist John Burrows remarks, “in its traditional forms, textual
analysis has to do with separating, distinguishing, and the like and it usually treats of single
works.” However, the forms of analysis I have tried to carry on in Hopkinsonʼs book, often aims
at gathering, combining and classifying ‒ a practice that in Digital Humanities is usuallyassociated with computational stylistics (stylometry) ‒ where the application of software like
Signature, stylo, of Stylene would be more fruitful.
Here is where the problem of the future direction of software useful to Digital Humanities
research ‒ and specifically for digital literary studies ‒ interject the crucial problem of rethinking
assumptions about textuality that have become established notion in structuralist and poststructuralist literary theory. To what extent is quantitative “style” representative of a specificauthorial subject/speaker regardless of its ontological status as an existing author or a fictional
character? To what extent is quantitative analysis software taking into account the conceptual
transformations of our notions of subjectivity when subjects materializes identity along theambiguous borders of inter-cultural linguistic domains? My paper ends with a few suggestions
for incorporating software functionalities that might face future transformation of the notion ofsubjectivity as it relates to language use.
3. Laura Michelle Hale, "Sports data revolution: A case study in closing the disability sport data
gap as a tool for improving sporting performance and increasing inclusiveness”
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As sports has moved to take greater advantage of the large volume of statistical data available
to do more complex analysis in the name of performance improvement, gaining competitive
advantage and talent identification, large gaps have emerged primarily in the area of womenʼs
sport and disability sport. These gaps are a result of lack of interest, lack of commercial value
in the data, and difficulties in accessing and storing these data. Consequently, womenʼs sportand disability sport are put at a further competitive funding and commercialization disadvantage
because efforts are concentrated in more the more lucrative menʼs sports.
For disability sports dependent upon performance data analysis to ensure fair play throughcontinual analysis of classification systems, this gap can result in a situation where certain
groups are underrepresented or put at a competitive disadvantage. This is particularly true incertain areas where people with different disability types such as cerebral palsy compete
against people with amputations and spinal cord injuries. The nature of their disability is suchthat muscle and strength performance mirror the latter groups, but less well understood is theimpact of cardiovascular endurance respiratory capacity. This can lead to people with cerebral
palsy posting inferior performances, resulting in further exclusion from sport across all levels ofsport. Large datasets including publicly available datasets that describe general disability
characteristics against sporting performance based on classification and gender can provide
greater insight, as opposed to the use of smaller scale data analysis that tends to be basedaround single events.
My paper explores the practical issues of creating a large-scale database for disability sport,
through a project being worked on to create a database of disability sports information. The
primary focus is on discussing data accessibility, the decision-making process for the software
used, questions around how accessible data should be, and balancing the desire for openness
to improve the lives of people with disabilities and improving their sporting performance withtrying to monetize these efforts to enable the project to continue. Secondary to this, the paper
explores some of the issues related to the construction of a large-scale sport database, thatcovers over fifty different sports for people from ten different disability groups with information
dating back to the 1920s.

Biography
Kristin Allukian (University of South Florida)
Kristin Allukian completed her B.A. at Mount Holyoke College, her M.A. at Trinity College inHartford, and her Ph.D. at the University of Florida. She spent two years as a Brittain
Postdoctoral Fellow in Digital Pedagogy at the Georgia Institute of Technology. Currently, she isan Assistant Professor of English and Affiliate Faculty of Womenʼs and Gender Studies at the
University of South Florida. Her primary research area is nineteenth-century American womenʼsliterature. Her current book project uses feminist historical context and archival research toanalyze representations of working women and labor systems in American womenʼs literatureand culture between 1839 and 1915. Her research and teaching interests include American
literature before 1900; womenʼs literature; and feminist digital humanities. Her work has
appeared in academic journals including, and.

Mauro Carassai (California State University, Northridge)
Mauro Carassai is Assistant Professor of Liberal Studies at California State University
Northridge where he teaches courses in Digital Humanities, Literary Theory, and American
literature. He was a Brittain Postdoctoral Fellow at Georgia Institute of Technology in 2014-15
and a visiting Fulbright at Brown University in 2007-2008. His research combines literary theory,
philosophy of language, and digital literatures within the larger frame of American literatures
and American studies. His scholarly work has been published in journals such as, and‒. He co-edited adouble issue for thetitled “Futures of Digital Studies” and he iscurrently at work on a manuscript exploring problems and perspective in configuring an.
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Laura Hale (ParaSport News, Spain)
Laura Michelle Hale completed her Bachelors of General Studies and her Masters of Science inInstructional Technology at Northern Illinois University, and completed her PhD in
Communications at the University of Canberra. She was the first person to serve as a
Wikipedian in Residence at a sporting organization, serving in this role for both the AustralianParalympic Committee and the Spanish Paralympic Committee. Her primary research interests
are social media research methodologies, and in women and disability sports. She has presented
at the 2012 Australia Cycling Tourism Conference, World of Football II, Melbourne titled, "Fillingan Information Void: Using Wikipedia to Document the State of and Promote Womenʼs Soccer inAfrica", and presented at a conference at the Australian Institute of Sport about Wikipedia. Asan accredited journalist affiliated with the Wikimedia movement, she has covered three
Paralympic Games, the 2012 Rollers and Gliders World Challenge, the 2012 IPC NorAm Cup, the2013 IPC Alpine Skiing World Championships and a few other events.

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Macroscopic Exploration of Large Text and Image
Collections via Similarity Heatmaps
Peter Broadwell, Tomoko Bialock (UCLA Library),
Hiroyuki Ikuura (Waseda University)
In the digital humanities, the term “macroscope” has come to denote a suite of interchangeable
tools that contribute to all levels of humanistic inquiry, from close to distant reading. The work
described here is part of an ongoing collaboration between faculty, researchers, and library staff
̶ which we call HYU:MA (ヒュー：マ) ̶ to realize the potential of the humanities macroscope in
the uniquely challenging but also highly rewarding context of Japanese digital scholarship.
For this study, we adopt analytical and visualization approaches commonly used in the biologicaland behavioral sciences ̶ specifically, similarity heatmap plots ̶ to build prototype tools forcomparing large digitized collections of Japanese texts and images. The ability to work with bothtexts and images is especially useful for Japanese cultural studies; accordingly, our researchdemonstrates the applicability of heatmap analysis to imperial anthologies ofpoems aswell as to Edo-period theatrical illustrations.

Similarity heatmaps and the humanities macroscope
Comparison is one of the primary modes of humanistic inquiry. We propose that similarity
heatmap visualizations (see Figure 1) are well suited to “macroscopic” comparative explorationsof large collections of digital objects. A heatmap plot visualizes the relative degrees of similarityof each item in a collection to every other item; the items are enumerated along the vertical (Y)and horizontal (X) axes, and the color at point (X,Y) indicates the degree of similarity between
items X and Y. The visualization can support any desired similarity metric; for text collections
these may range from purely stylistic aspects such as sentence length to content-oriented
measures like topic similarity. Crucially, our heatmap software features enable one to “zoom in”
to a pairwise comparison between any two items in order to investigate the precise nature of
their similarities and differences. Recent advances in open-source visualization software ̶
particularly the Plotly data-science suite ̶ facilitated the development of these features.

Text corpora:anthologies

We applied our comparison heatmap analysis to the, the 21 imperial
poetryanthologies compiled from 905 through 1439. The parallel construction of the anthologies ̶following the progression of seasonal topics and other genres established in the earliest
collections ̶ provides rich opportunities for macroscopic analysis via the heatmap interface.
Indeed, after ordering the poems sequentially and applying word- and topic-based similarity
metrics, we noted intriguing nuances of these parallel structures in the visualization, which weinvestigated via the heatmapʼs interactive “zoom-in” features (see Figures 1-3). Our scholarly
collaborators suggested that certain coloration patterns appearing over large portions of themap likely correlate with significant literary-historical trends, while other features are moredifficult to explain and present opportunities for new discoveries.

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Figure 1: A similarity heatmap of the texts of the, with groups of poems labeledby their anthology name and genre, e.g., 古今 ̲ 賀 .1 The area highlighted in red is shown in detailin Figure 2. Horizontal dashed lines show boundaries between the anthologies on the left axis.
Yellow shading indicates low similarity between texts, while blue indicates high similarity, basedon a TF-IDF weighted n-gram cosine similarity score from 0 (least similar) to 1 (most similar).
The color range has been tuned to highlight similarities. The dark blue “threads” running parallelto the main diagonal indicate parallelisms in the content of the poems in different anthologies.

Figure 2: A zoomed-in comparison heatmap of the poems of the(新後撰和歌集, left axis) and the(玉葉和歌集, bottom axis) anthologies, withcorresponding genre sections highlighted. The thickness of the diagonal “threads” indicates thedegrees to which the anthologies follow the same time-based topical progressions (early autumn
to late autumn, etc.) within corresponding seasonal genres. The comparatively large and varied
sections for “love” (恋) and “miscellaneous” (雑) poems, by contrast, exhibit fewer internalcorrespondences across anthologies.
1

The sequence numbers following the genres are unique to this visualization and should be
ignored.

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Figure 3: A “zoomed-in” comparison of the texts of 10 poems from each of the two anthologies
shown in Figure 2.

Image corpora:
The heatmap techniques described above for text corpora also are applicable to large image
collections, which is exciting given the prominence of visual art throughout much of Japanese
history and the availability of large digitized image collections online. In the future, it may evenbe possible to automate similarity analyses and heatmap generation comparing multiple image
corpora if the collectionsʼ metadata and derivative “thumbnail” images have been described andmade accessible via the Internet Image Interoperability Framework (IIIF) standard.
Software to find and track reuse of visual figures works well for print-based materials (seeFigure 4), while emergent deep learning-based approaches allow for comparisons that take into
account a broad range of stylistic factors (see Figure 5), rather than verbatim figure reuse.
Though such work remains experimental, the ability to track stylistic features over time andeven across media may contribute important new insights to digital art history and media
studies.

Figure 4: Detail view of the comparison heatmap for a set of kabuki playbills, highlighting pages
from two different playbills for which the upper portion of the original print was reused. Sharedvisual “keypoints” (also known as visual “words”) have been detected with a scale-invariant featuretransform (SIFT) and matched via an approximate nearest neighbor algorithm, with the similarity
values for the heatmap based upon the number of close matches between each pair of images.
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The materials shown here are a collection of 505 page images of playbills from kabuki theatersin Osaka () from the 1780s to 1870s. As with texts (see above), interactive
similarity heatmaps of image sets like these provide a comprehensive view of the pairwise
similarities across entire corpora, in contrast to scatter plots, clustering diagrams, and querybased search interfaces, which emphasize some relationships while obscuring others. The ability
to group and reorder the items compared on the heatmap and to “zoom in” to the comparisonof any two items also greatly facilitates the “macroscopic” exploration and study of a corpus.

Figure 5: A similarity heatmap for the same set of kabuki playbills from Figure 4, based upon
visual features extracted with the TensorFlow neural network library. Overall image similarities
were computed as the cosine similarities of the features of the “bottleneck” layer, as determinedby the Inception neural network. Playbill covers are compared to each other in the top rightsection of the heatmap. The highlighted pair of covers has few visual “words” in common (as in
Figure 4), but the neural network nevertheless identifies these images as being highly similar
stylistically.

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A Neural Network Approach to Historic Linguistic Change
Eun Seo Jo, Dai Shen, Michael Xing, Mark Algee-Hewitt (Stanford University)
Abstract
Historians have traditionally only used qualitative methods to identify historic moments oflinguistic change in discourse but this is limiting in objectivity and volume. Numericrepresentations of language allow us to quantify and compare the significance of discursivechanges and thus capture linguistic relations over time. We compare deep learning methods ofquantitatively locating when the usage of words in context changes. We introduce the methodof charting the moving average of perplexity in a language model trained on chronologicallysorted data. These results correspond to vectorized language we derive from a classification
model. We apply our models on a historical diplomatic corpus to find results showing that the
1910s and 1940s in American diplomacy proved to be notable moments in linguistic change.
With this example we hope to introduce applications of deep learning methods to Digital
Humanities usages.

Introduction & Literature Review
Humanists appreciate that linguistic change over time can reflect critical shifts in historiccontext whether they be in discourse, ideology, or access to communications technology[3][4].
Historians have relied on context to narrate the changing meaning of words and language.
George Chauncey shows how the tense Cold War atmosphere and the end of the Prohibition
instigated a narrower usage of terms such as ”gay” and change in gender classification from
cultural attributes to choice of sexual object [1]. Literary scholar Fliegelman argues that the
Declaration of Independence was written in a style that highlights oratorical impact including
rhythmic pauses and stresses [2].
This work proposes a deep learning approach to identifying moments of major deviations inlanguage. Traditional humanities works trace the meaning of words over time across multiple
archives through close-reading, such as those above.
Works in computational linguistics have introduced quantitative methods, the most relevant ofthese is Juolaʼs contribution which uses KL-divergence measures between pairs of documentsof differing year gaps at various time periods [4]. Juola shows that the 1950s were times ofsignificant linguistic change attributable to post-WWII culture and developments in television
technology. Juolaʼs work has two main shortcomings. The first is that KL-divergence alone does
not give a qualitative, historical explanation as to why there were changes and the author defers
to postulations of notable contextual events. Second, while we are able to quantify relative
linguistic change by time periods we cannot tease out single words to track their individual
evolution over time. Hamilton et.alʼs recent work addresses this limitation with word vectors.
They generate word embeddings from PPMI, SVD, and word2vec delineated by time period andcompare these embedding matrices across time by taking their orthogonal Procrustes solutions,derived from an SVD application (except the PPMI matrix)[5]. Using these results, they propose
several laws of linguistic evolution and most importantly showcase how individual words can betraced over time by comparing vectors from different eras[5]. Linguistic change has been a
topic of interest for the Digital Humanities community. Ted Underwood, for instance, uses LDA
topic modeling on a time segmented corpus to visualize the changing prevalence of generated
topics [6]. He charts the changing proportion of a topic of words through time as a topicalinsight to changing language over time.
Our main intervention is the use of deep learning to identify periods of dramatic linguisticchange at the token-level. Similar to Juolaʼs work, we are also examining language as a whole,though in future work we plan to trace the meaning of individual words. One reason why deeplearning approaches have been successful in NLP tasks is because the use of word vectors that
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encode multi-dimensional meaning of words based on co-occurrence with context words.
Because we initialize our model with pre-trained vectors, we utilize word meaning in our
generative RNN, giving higher overall accuracy. With word vectors, the perplexity scores reflect
not just the sequence of words but also the meaning of them. Another advantage of neural nets
over Juolaʼs KL-divergence methodology is computational convenience - that we can build themodel to run the entire chronologically ordered corpus through to compare the relative
perplexity scores across time, whereas Juolaʼs method requires each era to be computed andtested separately. Further, this paper also hopes to introduce a relatively under-utilized tool in
Digital Humanities ‒ neural nets, by way of an example.

Figure 1: Development set average perplexity over the five-year eras. The perplexity peaks at
two places through the years, 1915 and 1945, roughly corresponding to the start of WWI and
the end of WWII.

Our main approach trains an RNN language model over temporally sequential sets of training
data. We observe the changes in perplexity over these eras and find peaks at moments ofdramatic language shifts. We also test an RNN classification model that predicts the year bucketof a given document. Here we use the softmax weights to demonstrate topical and semanticchange over historical eras. The example corpus is a set of diplomatic texts containing historicalcommuniques.

DatasetWe collected 238,097 historical diplomatic documents that span from 1860 to 1983. We
performed stratified sampling to overcome the significant variance in frequency of documents
over the year buckets. We also capped the number of documents at 5000 to smooth out thedisproportionate representation of certain years. For both models, we use GloVe vectors with
400,000 vocab size of 100 dimensions.

Experiments
Document Classification
Recurrent classification model with GRU cells
We then trained and tested on an RNN classification model to classify documents into theircorresponding year buckets. The RNN classification model consists of a single layer of GRU
cells and prediction is made from the output of the last cell. Each GRU cell has the following
composition:

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'RFXPHQW&ODVVLILFDWLRQ5HFXUUHQWFODVVLILFDWLRQPRGHOZLWK*58FHOOVJADH 2017

Figure 2: Frequency of Documents over the Years. Documents were capped at 5000 per year.

)LJXUH)LJXUH

Figure 3: Histogram of the documents by token count. We chose to truncate all documents at
length 600 and pad those shorter than.

‫ ݖ‬௧ ൌ ߪ൫‫ ݔ‬௧ ܷ௭ ൅ ݄ሺ௧ିଵሻ ܹ௭ ൅ ܾ௭ ൯ ‫ ݎ‬௧ ൌ ߪ൫‫ ݔ‬௧ ܷ௥ ൅ ݄ሺ௧ିଵሻ ܹ௥ ൅ ܾ௥ ൯ ‫ ݋‬௧ ൌ ൫‫ ݔ‬௧ ܷ௢ ൅ ൫‫ ݎ‬௧ ٔሺ௧ିଵሻ ൯ܹ௢ ൅ ܾ௢ ൯ ݄௧ ൌ ‫ ݖ‬௧ ٔ ݄ሺ௧ିଵሻ ൅ ሺͳ െ ‫ ݖ‬௧ ሻ ٔ ‫ ݋‬௧ 

 



 

Following the last time-step we make the year prediction as follows: ൌ ሺ݄ሺ௟௔௦௧ሻ ܷ ൅ ܾଶ ሻ

 

where
is the probability distribution of the document being in each decade. We applied
dropout to prevent overfitting. We expect GRUs to have less of a problem with vanishinggradient and learn longer sequence data. To prevent exploding gradient, we integrate gradientclipping. We expect this model to better capture sentence structures than the plain bag ofwords (BOW) baseline.

/DQJXDJH0RGHO7

/DQJXDJH0RGHOJADH 2017

Language Model
Our second model is a language model that trains by predicting the next word given the current
and all previous words. We use the same RNN schematics as the previous model but make a
prediction at each time step. The output of each GRU cell is a probability distribution over
400,000 classes, our vocabulary size. The GRU composition changes to the following:‫ ݖ‬௧ ൌ ߪ൫‫ ݔ‬௧ ܷ௭ ൅ ݄ሺ௧ିଵሻ ܹ௭ ൅ ܾ௭ ൯ ‫ ݎ‬௧ ൌ ߪ൫‫ ݔ‬௧ ܷ௥ ൅ ݄ሺ௧ିଵሻ ܹ௥ ൅ ܾ௥ ൯ ‫ ݋‬௧ ൌ ൫‫ ݔ‬௧ ܷ௢ ൅ ൫‫ ݎ‬௧ ٔ ݄ሺ௧ିଵሻ ൯ܹ௢ ൅ ܾ௢ ൯ ݄௧ ൌ ‫ ݖ‬௧ ٔ ݄ሺ௧ିଵሻ ൅ ሺͳ െ ‫ ݖ‬௧ ሻ ٔ ‫ ݋‬௧   ൌ ൫݄ሺ௧ሻ ܷ ൅ ܾଶ ൯

 
 

We train our language model data in chronological order so that it is able to learn in sequence.
We divided them into half decade buckets. We make the first 85% of each decade as thetraining data and the last 15% of each decade as the development data (see figure 4). We
train(85%)-dev(15%) in sequence over the eras.

Figure 4: Diagram of Language Model Document Feeder: The 5 year corpora are fed into themodel in sequence, starting from 1890.

)LJXUH
Results
RNN Classification
With 30 epochs of training the RNN classification model, we were able to achieve f1 of over
80% as shown in figure 7. Figure 5 and 6 show the loss and accuracy respectively of the RNN
compared to the baseline model. The RNN outperforms the bag of words baseline consistently
throughout the epochs and reaches the peak performance after fewer epochs. This
demonstrates that sentence structure played a significant role in predicting the year of a given
document. We can also see the results of the RNN trained with the stop-words only corpus. The
accuracy performs surprisingly well, surpassing 50%, indicating that stylistic changes weresignificant enough to be captured.

Figure 5: Comparison of Dev set loss over 30 epochs for RNN and Feedforward models
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Figure 6: Comparison of dev set accuracy over 30 epochs for RNN, RNN with only stop words,
and Feedforward models

Figure 7: Test F1 Scores of RNN, Feedforward

Figure 8: Confusion matrix of the feed- forward baseline modelʼs prediction of year classes
after 30 epochs. The first class corresponds to the decade [1861,1870] and so on.

9JADH 2017

Figure 9: Confusion matrix of the RNN modelsʼ prediction of year classes after 30 epochs

Figure 10: PCA visualization of the decade classes from the softmax weights. Dimensions
reduced from 300 to 2. Note: 1870 is not graphed because the class bucket was missing data.

Figure 11: PCA visualization of decade classes from the softmax weights trained with non stop
words and punctuation . Dimensions reduced from 300 to 2. Note: 1870 is not graphed because
the class bucket was missing data.

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To visualize the historical trends in language, we took the softmax weights, thedimension [class hidden size] in:matrix of ൌ ሺ݄ሺ௧ሻ ܷ ൅ ܾଶ ሻ
and reduced the dimensions using PCA and charted the decade vectors in 10 and with only
stop words in 11. The visualizations show overall trends in language through the century and ahalf. Figure 10 shows how general language, including ideologies and issues of concern,changed over time. Figure 11 maps the changes in purely stop word usages over time, anattempt to focus on stylistic change. It is notable that while 10 shows greater variance inchanges from decade to decade, 11 appears more consistent, results which verify our
expectations about topics and style. Given that the dataset is a collection of diplomatic
documents, we labeled several notable historical events for interpretation. The distance between
1940 and 1950 in 10 is worth highlighting as post-WWII and the commencement of internationalorganizations such as the UN(1945), WHO(1948), and IMF(1945) changed American diplomatic
language to incorporate more vocabulary concerning global procedures. In figure 11 the jumpin style from 1900 to 1910 aligns with historical literature on diplomatic language. The
consensus is that eyeing the geopolitical and strategic potential of expanding the reach of
telecommunications coverage, the U.S. State Department made significant investments insubmarine cables during the first world war, which altered the constraints of telegraphic
expression [4].

RNN Language Model
The results of the RNN Language Model corroborate our results from the RNN classification
model. Figure 1 charts the changing dev set perplexity over the years at five-year intervals. If
language is consistent enough throughout the years we would expect the perplexity to fallconsistently as we feed more data. However, the results show perplexity spiking at certain
historical moments, in 1915 and 1945, indicating the change in language was dramatic enoughto confuse the model trained on the previous yearsʼ language. These two moments of spikealign with the our results from above.

Conclusions
The two RNN-based approaches ultimately produce similar results, showing that the 1910s and1940s were moments of significant linguistic change in American diplomacy. Our experiments
show that RNN models can be useful tools for measuring linguistic and topical change overtime. Finally, there are still many avenues to expand on the task of linguistic evolution. One
direction would be to engineer creative methods of interpreting the features of linguisticchange.
References[1] George Chauncey. Gay New York: Gender, Urban Culture, and the Making of the Gay MaleWorld 1890-1940. BasicBooks, New York, NY, 1994.
[2] Jay Fliegelman.
. Stanford University Press, Stanford, CA, 1993.
[3] David Paull Nickles.
. Harvard
University Press, MA, 2003.
[4] Juola, P. Computers and the Humanities (2003) 37: 77. doi:10.1023/A:1021839220474[5] Hamilton, William L., Jure Leskovec, and Dan Jurafsky. ”Diachronic Word Embeddings Reveal
Statistical Laws of Semantic Change”, 2016, https://arxiv.org/pdf/1605.09096.pdf.
[6] Ted Underwood and Andrew Goldstone. ”What can topic models of PMLA teach us about
the history of literary scholarship?”, 2012, https://tedunderwood.com/2012/12/14/whatcan-topic-models-of-pmla- teach-us-about-the-history-of-literary-scholarship

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Vector based measure of semantic shifts acrossdifferent cultural corpora as a proxy to comparative
history of ideas
Alexandre Gefen (Université Paris Sorbonne), Mark Andrew Algee-Hewitt,
David McClure (Stanford University), Frédéric Glorieux,
Marianne Reboul (Université Paris Sorbonne),
J.D. Porter (Stanford University),
Marine Riguet (Université Paris Sorbonne)
Our joint research group, a collaboration between Paris Sorbonne and Stanford University,
traces the parallel birth and evolution of key modern literary and aesthetic concepts in French
and English using vector based semantic analysis methods. This inter-institutional, multilingual,collaboration aims not only to explore the development of “literature” as a distinct discursiveand disciplinary field, but also to discover the mutual influence ̶ or, perhaps, surprising
independence ̶ of these two closely related national literary traditions. By sharing our methods
and results, we hope to foster the use of distributional semantics as a useful tool forinvestigating the comparative history of ideas in the literary field.
For this project, we have elected to compare two corpora of important and representative XIXthcentury magazines: in France, La Revue des Deux mondes (1829-1893), and in English, theBlackwood's Magazine (1817-1880), both influenced by the emergence of Romanticism. We
started with a simple word, « littérature/literature » (9100 occurrences in the Blackwood corpus,
12400 occurrences in the Revues des deux mondes) ̶ a word that we know has grownspecialized during the century ̶ in order to plot its semantic evolution and semantic space.
In this paper, we discuss the methods we have developed to:
Select, prepare and compare dissimilar corpora of old newspapers. After many discussions, we
identified these two magazines as the most equivalent single-periodical literary corpora in our
respective national literatures. To maintain this equivalence, we restricted the two corpora to thesame time period, 1830-1880. We preprocessed both corpora with custom methods conversion
to TEI encoding, and lemmatized them with Alix (Alix is a custom-designed lemmatizer and PartOf-Speech tagger) in French, with the Stanford CoreNLP tagger in English.
Graph the comparative historical evolution of our key word « littérature/literature » in vector
space using word2vec and Glove vectors. This builds upon our previous work with the HathiTrust
corpus in which we identified the lexemes with the most distinctive histories in relation toliterature. In this pilot attempt, we identified words that appeared frequently on the same pageas “literature” but with significant variation over time -- words that shifted from low to high levelsof correlation with “literature” across literary history, and vice versa. It is our contention that bymoving from this initial method to a vector-based analysis of our selected periodical corpora, wewill better be able to capture the nuances of the semantic field of “literature” over time.
Define a reliable way to measure polysemy. First, we build a set of chosen concept words with
their equivalent in both languages in WordNet. Eg. literature:littérature ; poetry:poésie ; art:art ;etc. Next, we build a set of bigrams for each of the words of this set, with the ten most
associated adjectives. We then compute the cosine distance between each bigram and apply
the methods used in Köper and Schulte im Walde 2014: once the cosine distance is computedfor each bigram vector, build a matrix with ranking proximity of those vectors, and compute
their mean rank. We are then able to determine a score that would weight the computing of theclustering coefficient of bigrams. As a low-rank bigram should in our assumption represent ahighly polysemic term, the lower the rank, the higher the weight on the clustering coefficient.
We then determine the clustering coefficient of each term in diachronic sequences, to draw adynamic representation in Gephi.

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This method allows us to find possible semantic attractors in the evolution of key literary
concepts and to plot the behaviour of specific semantic word pairs (for instance, littérature/
poésie and literature/poetry). In our final cultural interpretation, we compare semantic matrices
and historical evolutions in the two corpora/languages and confront the results with known
existing hypotheses in the history of aesthetic concepts in order to confirm the supposed
specialization of the field of literary studies. By focusing on concepts that undergo dramatictransitions during the period of study, our analysis brings together the study of diachronic
conceptual change and synchronic polysemy, allowing us to probe how multiple senses of aword coexisting in tension can eventually give rise to changes in meaning.

Bibliography[1] Boleda, Gemma, Sebastian Padó and Jason Utt. “Regular polysemy: A distributional model.”SemEval 12 (2012): 151-160.
[2] Hamilton, William, Jure Leskovec, J. and Dan Jurafsky. “Diachronic Word Embeddings Reveal
Statistical Laws of Semantic Change.” arXiv:1605.09096 [cs.CL] (2016)[3] Jorge-Botana, Guillermo, José A. León and Ricardo Olmos. “The representation of polysemythrough vectors: some building blocks for constructing models and applications with LSA.”
Int. J. Cont. Engineering 21 (2011): 328 ‒ 342.
[4] Köper, Maximilian, and Sabine Schulte im Walde. "A Rank-based Distance Measure to Detect
Polysemy and to Determine Salient Vector-Space Features for German Prepositions." LREC
9 (2014): 4459-4466.
[5] Mu, Jiaqi, Suma Bhat, Pramod Viswanath. “Geometry of Polysemy.” arXiv:1610.07569 [cs.CL](2016).
[6] Ravin, Yael, and Claudia Leacock (editors). Polysemy: Theoretical and Computational
Approaches. New York: Oxford UP, 2000.
[7] Springorum, Sylvia, Sabine Shulte im Walde and Jason Utt. “Detecting Polysemy in Hard andSoft Cluster Analyses of German Preposition Vector Spaces.” IJCNLP 6 (2013): 632-640.

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FAIRness for Citizens: Workflow and Platform forOpen Data with a Case Study on Edo Cooking Recipes
Asanobu Kitamoto(Research Organization of Information and Systems /National Institute of Informatics)
FAIRness in triadic co-creation
Data-driven science, or digital humanities, has three key stakeholders, namely researchers,machines and citizens, where three groups have complementary roles to cooperate, or what wecall triadic co-creation. Researchers deepen the body of knowledge by their expertise, machines
increase the size of knowledge by their computing power, and citizens enhance the diversity ofknowledge by their wide interest. Open data is a critical ingredient shared by all stakeholders,
but what is the best practice for promoting the usage of data by each stakeholder?
We suggest that a starting point to consider this issue is FAIR guiding principles [1], which was
proposed by FORCE 11 (the Future of Research Communications and e- Scholarship), a
community of scholars, librarians, archivists, publishers and research funders. FAIR stands for
Findable, Accessible, Interoperable and Reusable, and the guiding principles pay special
attention to “FAIRness for machines” to support automated analysis by computing agents. In a
triadic co-creation environment, however, the challenge is to generalize this concept to“FAIRness for researchers” and “FAIRness for citizens” to incentivize the usage of data by allgroups of stakeholders.
As an example, in November 2016, we released open datasets from Center for Open Data in the
Humanities (CODH) of Research Organization of Information and Systems (ROIS) in collaboration
with National Institute of Japanese Literature (NIJL) [2]. It consists of three datasets as follows.
ⅰ.

Dataset of Pre-modern Japanese Text (PMJT) [3]: digital images of 701 pre- modern
Japanese books fully digitized from the top to the bottom. Useful for reading anddownloading the original books.
ⅱ. Dataset of PMJT Character Shapes [4]: about 403,242 records of Unicode code point and
XYWH coordinates on PMJT images. Useful for training optical character recognition (OCR)
software.
ⅲ. Dataset of Edo Cooking Recipes [5]: about 100 recipes of egg dishes from the bookpublished around 1785. Useful for cooking.

Roughly speaking, (1), (2) and (3) datasets are targeting researchers, machines and citizens
respectively.
Each dataset should be designed to satisfy its own FAIRness criteria, but the biggest barrier torealize FAIRness for non-researchers is the language and characters. Text of old Japanesecharacters is readable only for researchers, and not for machines nor for citizens. For machines,
the lack of optical character recognition (OCR) software and manually transcribed text is abarrier for automated machine learning-based analysis. For citizens, the lack of fluent readers,which are estimated to be only a very small fraction of native Japanese speakers, is a barrier forunderstanding knowledge in the books. This means that the open datasets are literary notcitizen-readable data.

Workflow and Platform for FAIRness
As introduced, our target dataset, “Dataset of Edo Cooking Recipes,” is derived from a cooking
book published in 1785 (Edo Period) with the title[6]. It consists ofmore than 100 cooking recipes of egg dishes, and our motivation is to let citizens use them intheir daily lives and create derivative recipes to expand our knowledge about culinary culture in
Japan.
To reduce the barrier for citizens, we designed a workflow to increase the FAIRness.

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JADH 2017

ⅰ.
ⅱ.
ⅲ.
ⅳ.

Organize digital images in the Dataset of PMJT.
Transcribe text from digital images.
Translate old Japanese text to modern Japanese text.
Edit modern Japanese text into cooking recipes.

In the later stage within the workflow, the content of books is more human readable andaccessible. In addition, the final step (iv) improves the interoperability of knowledge
representation in terms of quantity, ingredients and tools.
In terms of quantity, a recipe in the original book lacks detailed descriptions about time andamount, probably because the book was published for professionals of the age. We however
added estimated quantitative information not in the original books to make the recipe actionable
for citizens by reducing both the risk of failure and the necessity of trial and error. In terms ofingredients, we changed some of old or local ingredients to similar ones that are easier to obtainnow. In terms of tools, we replaced old cooking tools to modern ones, such as refrigerator andfood processor, so that we can take advantage of todayʼs civilization. The last step is to add
photographs for each step, and the final photograph is well decorated for attractive impression.
Photographs help people imagine the whole process of cooking and convince the reproducibilityof the recipe.
Once the transformation of content is finished, where to deposit data is an important choice.
Our choice was to use Cookpad [7], the largest cooking recipe service in Japan, because it is aplatform which is already familiar with citizens in their daily lives. After depositing Edo Cooking
Recipes into Cookpad, we received unexpectedly large response from citizens through various
mass media, including television, radio, and magazines [8], and social media, including Twitterand Facebook.
We analyze that this success is because of better findability of the dataset in an interoperable
and actionable format on the platform where large number of citizens is already familiar with. In
addition, Cookpad has a function called “Tsuku-repo,” which is designed to share photographs
and captions about derivative recipes from the original one. To take advantage of this built-infunction, we could also enhance the reusability of data.

Figure.1: Cookpad Edo Cooking Kitchen

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POCiNP Approach
As introduced, the effective combination of workflow and platform improved the FAIRness of
cooking recipe data for citizens. We call this approach as “Putting Old Content into New
Platforms (POCiNP)” approach. When old books are digitized, their media are transformed fromatoms to bits, but the platform of content remains to be the same, namely book forms. A new
platform such as Cookpad requires the transformation of content because it is designed tooffer a new form of knowledge. A surprising observation is the inversion of freshness; once oldcontent was put into a new platform, citizens perceive old content as fresh content which wasnever seen on the new platform. We believe that the same approach can be applied to othercombination of content on platforms, but this is left for future work.

Acknowledgment
The author would like to thank Mrs. Ui Ikeuchi at University of Tsukuba for personalcommunication that led to the original idea of the paper.
References[1] Wilkinson, Mark D. et.al., The FAIR Guiding Principles for scientific data management andstewardship, Scientific Data, Vol. 3, 2016. doi:10.1038/sdata.2016.18.
[2] Asanobu KITAMOTO, Kazuaki YAMAMOTO, "Construction of trans-disciplinary data platformthat explores open data in the humanities", IPSJ SIG Computers and the Humanities
Symposium 2016, pp. 117-124, 2016-12 (in Japanese)[3] Dataset of Pre-modern Japanese Text (PMJT), http://codh.rois.ac.jp/pmjt/.
[4] Dataset of PMJT Character Shapes, http://codh.rois.ac.jp/char-shape/.
[5] Dataset of Edo Cooking Recipes, http://codh.rois.ac.jp/edo-cooking/.
[6] Mambo Ryori Himitsubako, 1785. doi:10.20730/200021712.
[7] Cookpad, https://cookpad.com/.
[8] CODH News, http://codh.rois.ac.jp/news/

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JADH 2017

FloraCultures: Conserving Plant-Based Cultural
Heritage in Australia through Digital Approaches
Paul Arthur (Edith Cowan University),
John Ryan (University of New England),Heather Boyd (Edith Cowan University)
Perth, Western Australia, is an urban area of high biodiversity. Botanist Stephen Hopper claims
that “Perth is one of the worldʼs most biodiverse cities, especially in relation to plants […] The
rate of discovery of new plants here, for example, is equivalent to the rate of discovery in manyof the rainforests” (cited in Perth Biodiversity Project, n.d., p. 1). With the increasing loss ofbushland to urban and suburban development, however, plant conservation faces a considerable
challenge. Yet, in protecting biodiversity, cities at the same time gain opportunities to conserve
the diverse forms of heritage, including cultural heritage, associated with their plants. This
heritage involves plants as food, ornamentation, medicine, and fibre; as literary, artistic andhistorical objects; and as sources of community memory, cultural identity, and personal wellbeing.
FloraCultures develops theoretical and practical approaches to conserving botanical heritage inWestern Australia. The project applies concepts of tangible and intangible heritage to plant
conservation. Intangible heritage suggests the “forms of cultural heritage that lack physicalmanifestation. It also evokes that which is untouchable, such as knowledge, memories andfeelings” (Stefano, Davis, and Corsane, 2012, p. 1). The 2003 UNESCO Convention for the
Safeguarding of the Intangible Cultural Heritage articulates five manifestations of intangible
heritage, including “oral traditions and expressions […] social practices, rituals and festive
events […] knowledge and practices concerning nature” (UNESCO, 2003, p. 2).
“Knowledge and practices concerning nature” is of particular relevance to the project. Scientificknowledge of Western Australian plants is available online through the website FloraBase andthrough texts such as Flora of the Perth Region (Marchant, 1987) and Perth Plants (Barrett andTay, 2016). Released by the Western Australian Herbarium in 1998, FloraBase is the“authoritative source” for plant knowledge in the state (Department of Environment andConservation, 2017). A comparable resource for Western Australian plant-based cultural
heritage is presently unavailable, and so FloraCultures aims to fill a significant gap. Notable
examples of digital plant-based heritage do exist elsewhere. For instance, the Native American
Ethnobotany Database, published by the University of Michigan, highlights plants as foods,
fibres, and medicines in Indigenous American societies (Moerman, n.d.). For Western Australian
species, some ethnobotanical information̶interview materials and Indigenous names̶isfeatured on the website Nidja Beeliar Boodjar Noonookury Nyininy or A Nyungar Interpretive
History of the Use of Boodjar in the Vicinity of Murdoch University, Perth, Western Australia(Collard, 2003).
Drawing from current developments in heritage protection, memory studies, and digital design,
this paper presents an overview of the projectʼs model for conserving Perthʼs botanical heritage.
The premise behind FloraCultures is that digital humanities approaches can render cultural
information about Perthʼs plants accessible. The project consolidates materials dispersed widely
across numerous traditions and sources, including Whadjuck Nyoongar (the Indigenous peopleof Perth) and colonial European expressions of knowledge and attachment to plants. Literature,art, historical accounts, and the memories of contemporary conservationists are brought
together under the umbrella of the research. Thus, a precedent for the conservation of cultural,social, literary, historical, and artistic knowledge of Australian biodiversity is developed.

17

JADH 2017

References[1] Barrett, R. and E.P. Tay. (2016). Perth Plants: A Field Guide to the Bushland and Coastal
Flora of Kings Park and Bold Park. Collingwood: CSIRO Publishing.
[2] Collard, L. (2003). “A Nyungar interpretive history of the use of boodjar in the vicinity of
Murdoch University, Perth, Western Australia.” Retrieved May 7, 2017, from http://wwwmcc.
murdoch.edu.au/multimedia/nyungar/menu9.htm.
[3] Department of Environment and Conservation. (2017). “FloraBase: The Western Australianflora.” Retrieved May 7, 2017, from http://florabase.dec.wa.gov.au/.
[4] Marchant, N. (1987). Flora of the Perth region. Perth: Western Australian Herbarium.
[5] Moerman, D. (n.d.). “Native American ethnobotany: A database of foods, drugs, dyes andfibers of Native American peoples, derived from plants.” Retrieved May 7, 2017, http://naeb.
brit.org/.
[6] Perth Biodiversity Project. (n.d.). What is the Perth biodiversity project? West Perth: Perth
Biodiversity Project. Retrieved May 7, 2017, http://www.lbp.walga.asn.au/Portals/1/
Templates/docs/background̲pbp.pdf.
[7] Stefano, M., Davis, P., and Corsane, G. (2012). Touching the intangible: An Introduction. In M.
Stefano, P. Davis & G. Corsane (Eds.), Safeguarding Intangible Cultural Heritage (pp. 1-5).
Woodbridge, England: Boydell & Brewer.
[8] UNESCO. (2003). Convention for the safeguarding of the intangible cultural heritage. Paris,
France: UNESCO.

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JADH 2017

Making a Mark: Tradition and Technology in the
Digitization of the Japanese Votive Slips Collection
at the University of Oregon
Kevin McDowell (University of Oregon)
Abstract
During the late Edo period a craze developed among
aficionados for commissioning the
production of highly decorative, multi-colored exchange slips (and then exchanging
them with fellow enthusiasts at regularclub meetings (n). After the Meiji
Restoration and the move towards modernization and Westernization, the practice of meeting to
exchange votive slips largely died down. However, sparked by a sense of nostalgia for Edo
culture and traditions,exchange clubs became active again and even spread outside ofTokyo to other urban areas, such as Osaka and Kyoto.
The University of Oregonʼs Knight Library holds one of the only collections ofoutside ofJapan and is now in the process of digitizing the entire collection of over 100 albums(approximately 4,000 individual images) adding metadata for each image and uploading therecords to the Oregon Digital Archive (https://oregondigital.org/sets/gb-warner-nosatsu).
Because most of the text is written in early modern Japanese style script, () the processof inputting accurate and robust metadata has been relatively slow and at this point only two of
the albums have been digitized, cataloged and uploaded. To speed up the pace of progress of
this project, the Japanese Studies Librarian at the University of Oregon has started working incollaboration with the Digital Scholarship Center to identify crowdsourcing transcription
software that will allow collaboration with specialists in early modern Japanese culture as well as
connoisseurs to add and edit metadata records for the UOʼs nōsatsu collection.
The twoenthusiasts who collected most of the votive slips in the UOʼs collection camefrom notably different backgrounds: Frederick Starr, an anthropology professor at the
University of Chicago, who became intrigued withduring his first visit to Japan in 1904
and Sato Masao, a self-employed woodblock printer, who was active in theworldfrom ca. 1920 to 1994.
Starr first joined themilieu around 1910 and then became so passionate about
attending meetings and collecting and exchanging votive slips that he became known as o-fudahakushi (Professor Ofuda).[1] His votive slip collection included not only the
he received
from other collectors at regular meetings, but also several albums dating back to the late Edo
period, with prints by prominent artists such as Utagawa Hiroshige and Utagawa Yoshitsuna.
After his death in 1931, Starrʼscollection was inherited by his sister, Lucy, who lived in
Seattle, Washington. Since, she, apparently, had no interest in keeping the collection she
contacted Gertrude Bass Warner, the primary benefactor of the University of Oregonʼs Art
Museum and a woman who was well known for her travels in East Asia and zeal for collecting
Asian art and artifacts. After negotiating via mail, Warner and Starr agreed upon a price for thecollection and Starrʼs votive slips moved from Seattle to the University of Oregon in theearly 1940s.
Sato, in contrast to Starr, who was a Western scholar [2] and complete outsider to Japaneseculture, was, by the very nature of his profession as a printer and in his enthusiasm for votive
slips, a sort of classic example of a typicalclub member. Since Satoʼs engagement withspanned several decades, the addition of his collection to the Starr materials gives the
University of Oregon a fairly comprehensive chronological coverage of the history of,ranging from the late Edo period (ca. 1840s) to the Heisei period (ca. 1994) with images

19

JADH 2017

depicting a wide variety of topics and themes, many of which harken back to traditional Edo
culture: Scenes from famous literary works; Kabuki; Famous places in Edo; Folklore; Street
performers; Sumo; Religious sites and pilgrimages. At the same time,
images from,
especially, the Meiji to early Showa era also provide glimpses of the massive changes that
occurred in Japan after the Meiji restoration as well as reportage on current events. One seriesshows a, sort of, short history of the Westernization and modernization of Japan, with prints
depicting telephones, electric lights, trains, subway stations and other newly established featuresof contemporary life. Another series focuses on the aftermath of the Great Kanto Earthquakeof 1923 as citizens tried to recover from the damage and chaos caused by the earthquake andthe massive fires that accompanied it.
As the digitization, cataloging and crowdsourcing of the UOʼs rare and culturally valuable
collection proceeds, the primary objective is to form a network of Japanese Studies scholars,museum curators, academic librarians andspecialists that transcends borders bymaking the images and metadata widely and freely available and enabling researchers to add
and enrich metadata. On a local level at the University of Oregon, a
research group,
which meets on a regular basis, has been organized. The members, including a professor ofearly modern Japanese literature, a Japanese art history professor, two graduate students inJapanese studies, and the Libraryʼs Japanese Studies Librarian and Japanese Cataloger, meet to
examine individual nosatsu albums, translate the cursive script and record metadata forcataloging. This group participated in a two-panel session devoted solely toat theAssociation for Asian Studies on the Pacific Coast Conference in 2017, which provided theopportunity to meet with other scholars interested inand opened up new channels forfurther expanding the network ofexperts. Finally, the Library received funding to hire a
graduate student for the 2017-2018 academic year to work 20 hours a week on the Universityof Oregonʼs
project. The student, working under the joint supervision of a Digital
Scholarship Center staff member and the Japanese Studies Librarian, will be charged withidentifying the most appropriate content management system for the project, recruiting
scholars andaficionados to participate in the metadata crowdsourcing effort andtraining participants in the use of the content management system.
In addition, this collection is rife with the potential for future digital humanities projects, such as
charting networks of Edo, Meiji and Taisho period collectors by the groups they belonged to or
mapping Starrʼs extensive travels around Japan as he made pilgrimages, toured the old Tokaido
Highway and barnstormed across Japan [3]20JADH 2017

Language Documentation and Standards in Digital
Humanities: TEI and the documentation of Mixtepec-Mixtec
Jack Bowers (Austrian Academy of Sciences), Laurent Romary (INRIA)
This paper discusses an ongoing language documentation project covering the Mixtepec-Mixtec
variety of Mixtec (iso 639-3: mix)11 among the primary facets of which are to: create an open
source body of reusable and extensible multimedia language resources (LR) encoded in TEI andin accordance with the recommendations of Bird & Simmons (2003); use the above in order tofurther the knowledge of all aspects of the language itself within the fields of linguistics andlexicography by producing empirical corpus-based descriptions and analyses of various aspectsof the languageʼs features; demonstrate and test the application and utility of descriptive
features from cognitive linguistics theory2 in the annotation of the LR; demonstrate and evaluate
the application of encoding and description standards on a collection of lexical and knowledge
resources for an under-resourced non-indo-european language.
Thus far, the majority of the projectʼs data are collected from recordings of consultation
sessions with native speakers, and a collection of childrenʼs texts in the languageʼs working
orthography published by the Summer Institute of Linguistics (SIL) Mexico. The recorded
speech has been transcribed using Praat (Boersma and Weenik, 2017), then converted to TEIXML using XSLT. The relatively small dataset 33 allows for ambitious goals with regards to thefeasibility of applying an extensive degree of different annotation/markup to our dataset.
Concretely, the output include a collection of primary and secondary resources, specifically:
audio files, digital TEI dictionary with English and Spanish translations, etymological information(where possible), TEI digital editions of the SIL Mexico publications with multi-level annotations,semantically and grammatically annotated corpus of all available original and external written LR,
TEI utterance files with transcriptions and annotations, evidence based inventories anddescription of lexical features using TEI feature structures. Where possible, we make use ofopen source standard vocabularies such as ISOcat4, which is the local reference point for the
corpus annotation, feature structure inventories are created for the following lexical features:phonetics/phonology, morphophonology and morphosyntax and morphosemantics, a conceptual
inventory for semantic profiles of Mixtepec-Mixtec vocabulary to be based in Linked Open Data
resources such as dbpedia, Wikidata, etc.
As is typical in language documentation and lexicography, the organization and representationof the projectʼs data involves collecting and integrating a wide array of linguistic, geographical,
cultural, historical, and scientific information. While all of these things have been addressed
before, there is less precedent for them all being attempted together within the TEI framework.
Whereas the TEI has been widely applied to projects involving mostly indo-european languages,
there has been few attempts to apply it to under described and indigenous languages;
Czaykowska-Higgins et al. (2014) is the most noteworthy to date (see also: Bates & Londsdale
2009; Thieberger 2013). There has also been a lack of applications of the TEI to tonal languages
such as Mixtec.

1

Mixtepec-Mixtec ( ʻSaʼan Saviʼ : ʻrain languageʼ ) is an Otomonguean spoken by roughly 900010000 people in the Juxtlahuaca district of Oaxaca, and parts of the Guerrero and Puebla statesof Mexico. Most of the spoken data collected in this project originate from consultation sessions
with two native speakers from a town called Yucanani (17.30083, -97.89389), which is part ofthe municipality of San Juan de Mixtepec with a population of around 500 people.
2
In particular concepts from Langacker 1987, among others will be used and cited in full infinal paper3
Thus far the project contains roughly 1000 original and collected open source sound files ofvarious length, the vocabulary collected from which an combination with the SIL documents is
expected to be in the low thousands.
4
The ISOcat (ISO TC37) is currently undergoing a process of remodeling, editing and migration
to a new management system ʻTermWebʼ (Wartburton, 2015).
21

JADH 2017

Additionally, applying these methods, technology and standards to MIX is also challenging due
to a lack of established linguistic description5 and external language data; the orthography is not
fully conventionalized6, and it does not represent the lexical tone. This results in an extremely
large number of homographs. The combination of these issues and the significant amount ofphonetic variation encoded in the data makes glossing from sources, and applying NLP
processes to search, retrieve and analyze highly difficult.
As mentioned, a major goal of this work is to further the body of knowledge of all aspects of
the languageʼs features. Given that linguistic subfields do not exist separate from one another,and they interact and overlap both synchronically and diachronically, it is important that themarkup and annotation strategy used is able to capture all of these phenomena. Evidence forthis can be seen in the fact that in MIX, changes in tone can indicate such morpho-semantic
features as: negation, tense, person, possession or accompaniment. Thus we need a markup
system that can handle overlapping features flexibly.
For both text sources and transcribed utterance files, standoff annotation provides a flexible
means in which to flexibly and efficiently express the necessary linguistic and conceptualfeatures in our data, including linguistic interface phenomena. For this purpose we adopt therecommendations of ISO 24624 (2016) and Schmidt (2011) as a baseline for our standoff
annotation of spoken language transcriptions, integrating the ongoing work of Ba㶠ski et al.
(2016) regarding an expansion and refinement of the TEI standoff annotation system.
The need to process our spoken language data from sound files while providing an evidence
based description of the languageʼs phonetic and phonological systems requires the use ofsoftware tools that enables us to create annotations, and produce data that can be transferred
to TEI. However, given our specific needs, there is still no perfect choice. Due to a combinationof legacy reasons, and the desire to provide a quantitative body of evidence for the descriptionof MIX phonology, we use Praat, despite the fact that this software is primarily geared towards
the fields of applied phonetics/phonology, and is rarely used in digital humanities. Accordingly itlacks options for TEI or even basic XML output, and doesnʼt have the capacity to include key
metadata with transcriptions and sound files. In our paper we will elaborate on why we chose
Praat over other tools such as EXMARaLDA (Schmidt, 2004).
In our paper we describe basic facets about the MIX language, the data and give examples ofmarkup methodologies for each stage of the workflow. Along the way we point out issues
encountered with the relevant markup standards, vocabularies, resources and tools, discuss
future and/or ongoing efforts to find solutions. Additionally, we provide insights into how wehave attempted to navigate the need to converge the technical needs of the data, with the needto seamlessly describe the languageʼs features according to relevant theoretical linguistic
principles. Where the TEI and other relevant standards do not meet our needs, we provide
concrete use cases and input for proposed improvements. Our efforts attempt to not only
provide a lasting and reusable set of resources for the MIX language, but also to make strides
towards bridging the gap between lexicography, language documentation, theoretical linguistics,
computational linguistics and digital humanities.

Works Cited[1] Ba㶠ski, P. (2010). Why TEI stand-off annotation doesnʼt quite work. In.
5There have been previous descriptions of MIX phonology (see: Pike & Ibach 1978; Pastor
2005, 2010; Pastor & Beam de Azcona 2004a, 2004b); however none of these provided any
corpus, acoustic or quantitative data. Our analysis has some significant differences that we willarticulate in future publications based on empirical evidence.
6
The executive council of the Academy of the Mixtec language has established a working
blueprint for a standardized orthography for Mixtec languages (Consejo Directivo de Veʼe Tuʼunsavi. A. C, 2011, n.d.). However given the diversity in the varieties of Mixtec, some of which are
not mutually intelligible, this directive is far from establishing a systematic governance oforthographic standards. The orthography currently used in the project is based on the de-facto
conventions used by SIL Mexico.
22

JADH 2017[2] Ba㶠ski, P., Gaiffe, B., Lopez, P., Meoni, S., Author, L., Schmidt, T., Stadler P. and Witt, A. (2016).
Wake up, standOff! Presented at the TEI Conference and Membersʼ Meeting, Vienna.
https://hal.inria.fr/hal-01374102[3] Ba㶠ski, P., & Przepiórkowski, A. (2009). Stand-off TEI annotation: the case of the National
Corpus of Polish. In(pp. 64‒67).
Association for Computational Linguistics.
[4] Bates, D., & Lonsdale, D. (2010). Recovering and updating legacy dictionary data. In(Vol. 27, pp. 1‒12).
[5] Boersma, P., & Weenik, D. (2017). Praat, a system for doing phonetics by computer (Version
6.0.28).
[6] Consejo Directivo de Veʼe Tuʼun savi. A. C. (2011). Pronunciamiento de Veʼe Tuʼun Savi, A. C.
“Academia de la Lengua Mixteca” respecto a TuʼUn Savi (Lengua de la Lluvia). Retrieved
from https://goo.gl/mnLrWt[6] Consejo Directivo de Veʼe Tuʼun savi. A. C. (n.d.). Ndusu. INALI. Retrieved from http://www.
inali.gob.mx/pdf/ndusu.pdf[7] Czaykowska-Higgins, E., Holmes, M. D., & Kell, S. M. (2014). Using TEI for an Endangered
Language Lexical Resource: The Nxa㷊amxcí n Database-Dictionary Project.
, 8, 1‒37.
[8] Drude, S. (2012). Digital Grammars -. University of Hawaiʼi Press.
[9] ISO-24624. (2016). Language resource management ̶ Transcription of spoken language.
[10] Langacker, R. W. (1987).
(Vol. 1).
Stanford university press.
[11] Paster, M. (2005). Tone Rules in Yucanani Mixtepec Mixtec (p. 13). Presented at the SSILA
meeting, Oakland, Ca: SSILA.
[12] Paster, M. (2010). The role of homophony avoidance in morphology: A case study from
Mixtec. In(Vol. 21, pp. 29‒39).
[13] Paster, M., & Beam de Azcona, R. (2004a). A Phonological Sketch of the Yucunany Dialectof Mixtepec Mixtec. In(pp. 61‒
76). UC Santa Barbara.
[14] Paster, M., & Beam de Azcona, R. (2004b). Aspects of tone in Yucunany Mixtepec Mixtec.
Presented at the Conference on Otomanguean and Oaxacan Languages, University ofCalifornia, Berkeley.
[15] Pike, E. V., & Ibach, T. (1978). The phonology of the Mixtepec dialect of Mixtec.
(Descriptive Linguistics, Vol. 2, pp. 271‒
285). The Hague: Mouton.
[16] Schmidt, T. (2004). Transcribing and annotating spoken language with EXMARaLDA. In.
[17] Schmidt, T. (2011). A TEI-based Approach to Standardising Spoken Language Transcription.
, (Issue 1).
[18] Thieberger, N. (2013). Reusing manuscript vocabularies, an example from Western
Australia. Presented at the Conference presentation at the 3rd International Conference on
Language Documentation and Conservation (ICLDC 3), University of Hawaiʼi.
[19] Wartburton, K. (2015). Re-modelling the ISO T37 data category registry: current status,
challenges, and theoretical reflections.
, (1).

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JADH 2017

Application of the Concept of “Linkbase” for the
Digitalization of Linguistic Resources and Analysis
Yona Takahashi (University of Tsukuba)
Masakatsu Nagai (University of Tokyo)
Toshihito Waki (University of Tsukuba)
The presentersʼ goal was to establish a new linguistic field and its methodology based on a
photographic image database for written materials. Specifically, two types of textual data werecreated and stored into a database. Since 2015, the authors have begun to develop a bilingual
database of the Egyptian-Akkadian treaty texts written in hieroglyphics and cuneiform, whichwere made by the Egyptian empire with the Hittite empire in the fifteenth century B.C.E., toanalyze the grammatical correspondences between the Egyptian and Akkadian languages. In
addition, since 2016, the authors have started to develop another digital archive of the literarytext, “Epic of Gilgamesh,” which was written on cuneiform tablets and is widely known for morethan two thousand years in the Ancient Near East. Since there are various editions of the epic
which remained from each era and each region, it is necessary to compile them to describe theirrelationship.
The primary issue which needs to be considered is: how to describe many-to-manycorrespondences between resources as digital data? In the bilingual database case, thegrammatical correspondences are not usually one-to-one, but many-to-many due to thedifferences in the grammatical systems between the two languages in order to clarify the
linguistic features by contrastive analysis. In the case of digital archiving for the literary text, it isoften that a part in a previous edition was divided into some divisions and rearranged in thesubsequent editions, and vice versa. In order to comprehend continuous editing done by theancient poets, the authors attempted to visualize the intermittent and many-to-manycorrespondences among the editions or their internal structures. For these reasons, a standard
framework was necessary to describe the many-to-many correspondences.
To resolve this issue, the authors adopted the concept of “linkbase,” originally proposed in XLink
1.0. A linkbase is a "link database" which is a collection of third-party links where neither thestarting resource nor the ending resource is local. Utilizing this linkbase, the authors would be
able to express links which reside in a location separate from the linked resources. Linkbase
makes link management easier by concentrating on document management while separating a
number of link collections from the related documents.
However, the authors did not adopt the whole specification of XLink. Furthermore, the concept
of “@actuate” (for the representation of the link) not need. Even XML syntax is not necessarily
required. The main point is that a “link database” can be treated as the same object as a“document database” and an “image database.” Therefore, written materials, photo images, andlinking relations can be digitalized uniformly, with accustomed digital tools such as Microsoft
Excel, OpenOffice Calc, etc. at a low learning cost.
Then, the authors created a small web-based collation tool using HTML and XSLT to visualize the
many-to-many links, in order to verify the complex linking structures and, if necessary, correct
the data. The data was then provided in a highly sharable XML format conform to Text Encoding
Initiative (TEI), not only for the document but also for the linkbase. For this purpose, TEI has<linkGrp/> element ( 16.1) and Pointing mechanisms ( 16.2). Especially the latter mechanisms
allowed us to utilize any types of linking representation by converting them into canonical URI/
IRI references ( 16.2.5).
The semantics of the links (i.e. “@arcrole” in XLink) is still an open issue. Only two types of link
are required for our purpose: direct correspondence and indirect/informative correspondence.
As a consequence, it is sufficient for us to prepare two independent linkbases so far, by set
24

JADH 2017aside a complicated typology of links. A further discussion might be needed for thecomprehensive digitalization in our databases.
The concept of “linkbase” does not depend on the methods or operations to utilize data. It
allows us to choose a suitable data format for our own purposes, and leads us to achieveinteroperability of the various open data by linking, even by linking of “link databases.” Further
discussions and use cases on linkbase will be useful for our future.

25

JADH 2017

TEI/XML Markup of Engi-shiki as Research Platform
for Historians of Ancient Japan
Naoki Kokaze (University of Tokyo),
Kiyonori Nagasaki (International Institute for Digital Humanities),
Makoto Goto, Yuta Hashimoto (National Museum of Japanese History),
Masahiro Shimoda, Albert Charles Muller (University of Tokyo)
As part of the collaborative research project with the National Museum of Japanese History, thispaper aims to report a joint research achievement through examples of TEI/XML markup, basedon ʻTransactionography,ʼ for an ancient Japanese historical sources called, which wascompiled as an administrative manual in the tenth century.
According to one of the most influential historians on the, Toshiya Torao, detailed
rules on society and administration during that period were regulated very precisely. For
instance, there were detailed regulations covering rituals and festivals held in various parts ofJapan, designation of offerings of rituals, tributes and taxes paid to thegovernment,
and allocation of Shōzei and Kugai (rice plants used as funds which were distributed to eachadministrative region). Due to the nature of the descriptions of such diverse details, theresearch subjects tend to be subdivided, and thus it is difficult to obtain comprehensive views
on society at the time.1
In order to offer a possible solution for this historiographical obstacle, this presentation will
provide some visualizations, such as a geographical comparison of each administrative region
based on its financial scale, and variations in the processing methods of major products, e.g.
abalone, through the text markup using the TEI/XML methodology ʻTransactionography,ʼ firstdeveloped by Drs. Tomasek and Bauman.2
This is an extended model of TEI used to mark up appropriately various kinds of Historical
Financial Records (HFRs) containing histories of the exchange of goods and services, such asreceipts or account ledgers.
In this paper, two examples can be addressed as a result of text markup of. At first;
when it comes to marking up Shōzei and Kugai rice plants, they can be expressed as budgets
that each region could utilize for its administration.
Markup. 1: Shōzei and Kugai Rice Plants

Taking into consideration the concept of ʻTransactionography,ʼ we define the meanings of Shōzei
and Kugai budgets as transfer of measure from people to each county. In this markup strategy,
people can be understood as inhabitants of the counties.

1

Toshiya Torao, Engi-shiki no Kenkyū (A Study on Engi-shiki), Tokyo: Yoshikawa Kōbunkan,
1995.
2
Kathryn Tomasek and Syd Bauman, ʻEncoding Financial Records for Historical Researchʼ,
Journal of the Text Encoding Initiative [Online], Issue 6 | December 2013, Online since 22
January 2014, connection on 1st May 2017. URL : http://jtei.revues.org/895 ; DOI : 10.4000/
jtei.895.
26

JADH 2017

To make use of these inline and standoff markup texts, we connect them with geographicalinformation of each county seat, thus obtaining a visualization such as follows:

Figure. 1: Financial Comparison of Administrative Regions based on the Amount of Shōzei andKugai Rice Plants3

Second, we mark up the products. In thei, we can see a variety of products emerging
in various parts of detailed regulations used as offerings or taxes. Among them, abalone is oneof the most circulated foods.

Markup. 2: Hierarchical Marking up of Abalone

One important point in this markup is multilingualism. To offer the variation of products name(which means the variety of processing method of food as well as the places and regions of
production), all terms referring to abalone should be marked up with <measure> element
containing the type ="abalone" attribute written in English, and subsequently, the commodity
attribute can be used to address precisely the name of products in Japanese. Consequently, we
can comprehend the variety of names signifying abalone, as follows:3This mapping visualization is created with cartoDB (https://carto.com/).
27

JADH 2017

Figure. 2: Variations of the name referring to Abalone in the

Apart from these example data manipulations, the important contribution of our study will be toformulate a research platform enabling historians to offer various aspects of society andadministration in ancient Japan, according to the concerns of individual researchers or research
projects.
As a future vision of collaborative projects with the National Museum of Japanese History, it isexpected to clarify the co-relationships between images and actual texts by exposing such atext database together with the image database based on IIIF (International Image
Interoperability Framework), and to expand the possibility of making the best use of historicalmaterials. The progress of our ongoing collaborative research project should make it possible todisseminate the accumulation of Japanese ancient historical research more effectively andinternationally.

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JADH 2017

Ontological Engineering in Philosophy. Example ofPhenomenological Data
Raphael Kur (Jagiellonian University)
Digitization of philosophy is already a fact. Millions of texts have been archived and madeavailable to users, typically on the world wide web. In my paper, I would like to deepen themeaning of the term “digitization of philosophy”, since the process of digitization is not limited
to text scanning, resulting in a graphic file (bitmap). Scanning is followed by a series ofimportant processes like data (graphic) compression and an automatic process of recognitionof characters and words using the OCR (Optical Character Recognition) method. Still, the mostimportant element in the whole process of digitization is the final product made of parts that
underwent selection, organization, and categorization. Just like any genre of literature requires
navigation, so does philosophical literature, to make it structured and user-friendly.
On one hand, the idea I am presenting in this article is not novel. Some institutions are alreadyworking on this topic, using ontological (philosophical) ideas and computer methods, usuallyapplied to categorize knowledge in biomedicine. This particular type of research is referred toas ontological engineering. Institutions active in this area are for example Metaphysics Lab at
Stanford University, Ontology Research Group at University of Buffalo (New York), or
Laboratories for Applied Ontology in Italy (Trento). On the other, it is only the Indiana Philosophy
Ontology (InPhO) project, carried out at the Indiana University Bloomington that applied
ontological categorization covering philosophy in its entirety, albeit in a very general manner,
operating basic information.
Ontologies are representations of entities and relationships between them. Engineering means atechnical proposal for solving a concept. Many of these ontologies are based on natural
language though the philosophical text corpus is highly specific, often containing ambiguous
terms. Therefore, building a simple, yet transparent structure, and at the same time reflecting
the richness and meaning of philosophical concepts is not a simple task. Since ontologicalengineering of philosophy is not that widespread, we should expect several approaches andconcepts in this area.
It would seem that the best place to start categorizations is from the most characteristic
elements of a genre of literature, in this regard from one philosophical concept and its evolution,
including relationship with other philosophical fields. Accordingly, what I would like to present is
a proposal of a selected philosophical trend on a general, meta-level.
Contemporary philosophical literature consists of a large number of diverse publications,
ranging from dictionaries, encyclopedias, books, to articles and abstracts. All these containkeywords and indexes, required when searching for information within the text, however, a muchhigher level of order is required, general and abstract enough to be able to connect even themost unassociated ideas. This is especially important today when we are flooded withinformation, likewise in the field of philosophy. Ontological organization in the digitized world ofphilosophy is needed today more than ever.
Philosophy has many branches of ideas, and various kinds of divisions of the general subdisciplines (metaphysics, ethics, aesthetics, axiology, political philosophy, philosophy of science,philosophy of mind, etc.) derived from authors of (e.g. Platonism, Confucianism,
Schopenhauerian philosophy, Kantianism; phenomenology from the point of view of Husserl,
Heidegger, Ingarden, etc.). Combinatorics in this regard is very diverse as a result of many
variables and aspects (e.g. geographical, cultural, ethnic, religious, linguistic, etc.), not forgettingthat the selected concepts may be found by using a specific time and location (e.g. thenineteenth-century aesthetics and neuroaesthetics, Japanese and Polish phenomenology, etc.).
It makes sense, therefore, to present this type of information in the form of a tree. This method29

JADH 2017

has long been used in logic and works in computer science. This type of structure of informationis accessible and easier to formalize at different levels (from the most general to detailed),followed by implementation of programming. Consequently, in the near future, when we attainlarge collections of philosophical information, programs, operating efficiently on large bodies oftext will be more useful in specific, specialized research.
These types of hierarchical structures should be separated by specific ontological concepts andinclude specific links/relations. The following diagram is a proposed example. Phenomenology asa philosophical movement, categorized by authors, varieties of thought (ideas), geographicalextents, and temporal frames. By creating general schemes, we can create additional detailed
levels (branches), then try to implement their empirical methods using logical indicators, in thisway we decrease the proximity from methods of programming.

Figure. 1: Phenomenology segmented along geographical, philosophers and temporal axes withthe resultant cross-segmentation

References[1] Arp, R., Smith, B., & Spear A.D., (2015).
Cambridge, Massachusetts, London; MIT Press.
[2] Smith, B., (2003). Preprint version of chapter “Ontology”, in L. Floridi (ed.),Oxford; Blackwell, 155‒166.
[3] Sowa, J. F., (2000).
. Pacific Grove, CA; Brooks Cole Publishing Co.
[4] Russell, S., Norwig, P., (2010).
. Prentice Hall.

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JADH 2017

Development of a title authority database for
Chinese classic works
Maiko Kimura (University of Tokyo)
BackgroundThe(FRBR) and its revised version, the(LRM)1 demonstrate a new model for library cataloging. However, it isquestionable whether the FRBR model is applicable to Chinese classics in libraries, as the model
was developed by The International Federation of Library Associations and Institutions (IFLA),
whose member libraries are mostly from Western countries2. As such, feasibility research on theadaptation of the FRBR for Chinese classics is lacking.
Although the LRM emphasizes authority control, authority data for work titles has hardly beencreated in Japan, as the(typical Japanese cataloging rules that have
not yet adopted the FRBR) do not require it. Currently, no comprehensive data is available inJapan for Chinese classic works.

PurposeThe purpose of this study is to create comprehensive title authority data for Chinese classic
works, adopting the simplified FRBR model for Chinese classics. In addition, the author defines
metadata vocabularies to release the data as Linked Open Data (LOD).

Model Design
As shown in Figure 1, compared to the relationships among entities representing products ofintellectual or artistic endeavor in the FRBR model, in the proposed model, which is specificallyfor Chinese classics, these relationships are simplified.
Although most bibliographic records created in library catalogs are at the Manifestation level(see Figure 1), Item-level records have been created for classic books, as the characteristics ofeach item such as interpolations, seals, binding, and so on are important in classic works.
Therefore, libraries tend to create bibliographic records at the Item level for classic books, atleast in Japan.

Figure 1: Comparison between the FRBR model and proposed model31

JADH 2017

In addition, the concepts of Expression and Manifestation are not always appropriate for
Chinese classics. For example, although a partial re-carving of a printing block is important forusers of Chinese classics, it is unclear whether we should perceive only one spot of the recarving as a difference in Manifestation in the FRBR.
Bibliographers and researchers organize published, printed, or revised Chinese classic works in
traditional classifications or orders. Library communities should carefully consider if we cansimply change the results of their research into the series of Work, Expression, Manifestation,and Item. The proposed model only adopts Work and Item, leaving sufficient space for theutilization of research results by bibliographers in the future.

Developments
Recording work titles
3
A title authority database for Chinese classic works, namely the, was developed
4
based on, which lists various titles for the same work. Since the bookdoes not show a representative title (preferred title) among several titles for a work, the author
selected the representative title for each work according to the following order of priority. 1.
The title that can return the most search results in theis preferred.5
2. The title not used in other works is preferred. 3. The title that well represents the subject of
the work is preferred. To distinguish different works sharing the same title, the second priority
takes precedence over the first for some works. This means there is no duplication among
representative titles. Both representative and non-representative titles are recorded in the
database.
is written in simplified Chinese characters. However, the author inputs all
titles for each work in traditional Chinese characters, simplified Chinese characters, and(Romanization for the Chinese language) to improve data searchability.

Linking Work to Item
Here, CiNii Books6 is searched using the representative title and non-representative titles foreach work. Then, appropriate bibliographic records are selected and imported through the
OpenSearch API of CiNii Books.7 This means that each work has item titles that are exemplarsof the work, allowing users to retrieve them using the title and alternative titles of the Item andWork titles. Although the main purpose of importing bibliographic records is to obtain the title
and alternative titles of each item, author names, publication years, publishers, holdings, notes,
and the URL of the bibliographic record in CiNii Books are also recorded in.
When CiNii Books does not return results, the link cannot be made, and only the work record
exists in the database. As of June 12, there are 200 work authority records in, and
73 of these are without item titles.

Towards LOD
Figure 2 illustrates a current plan for metadata vocabularies to represent
ʼs data. It
adopts some vocabularies from the SKOS extension8, DCMI Metadata Terms9, BIBFRAME 2.010,
and original defined vocabularies. The prefixes of the original vocabulary are <rep:> forrepresentations and <kwma:> for the proposed model, and their URLs temporally do not exist. It
differentiates transcription from the transliteration of each language, and considers a title inother languages as different titles. Thus, all titles other than a preferred title with a<skosxl:preflabel> property have a <skosxl:altlabel> property.

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JADH 2017

Figure 2: LOD vocabularies for-sanAcknowledgement
This work was partially supported by Konosuke Matsushita Memorial Foundation Research
Grants 2016 and JSPS KAKENHI Grant Number JP 17J40023.
Reference[1] IFLA. (2017).
https://www.ifla.org/files/assets/cataloguing/frbr-lrm/ifla̲lrm̲2017-03.pdf[2] IFLA. (2015).
. Retrieved from https://www.ifla.org/files/assets/hq/
annual-reports/2014.pdf[3]. Retrieved from https://zoshoin-db-zosan.herokuapp.com/works[4] 杜信孚, 王剑编. (2000). 同书䇗名汇录. 江苏古籍出版社.
[5]. Retrieved from http://kanji.zinbun.kyoto-u.ac.jp/kanseki[6] National Institute of Informatics (NII).
. Retrieved from http://ci.nii.ac.jp/
books/?l=en[7] NII. CiNii Books: Metadata and API:CiNii Books OpenSearch for Books & Journals. Retrieved
from https://support.nii.ac.jp/en/cib/api/b̲opensearch[8] W3C. SKOS Simple Knowledge Organization System eXtension for Labels (SKOS-XL)
Namespace Document:HTML Variant. (2009, November 27). Retrieved from https://www.
w3.org/TR/skos-reference/skos-xl.html[9] DCMI. DCMI Metadata Terms (2012, June 14). Retrieved from http://dublincore.org/
documents/2012/06/14/dcmi-terms/[10] The Library of Congress. BIBFRAME 2.0 Vocabulary List View. Retrieved from http://id.loc.
gov/ontologies/bibframe.html

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JADH 2017

Universal Dependency for Modern Japanese
Mai Omura (National Institute for Japanese Language and Linguistics),
Yuta Takahashi (Meiji University), Masayuki Asahara (National Institutefor Japanese Language and Linguistics)
Universal Dependency (UD; McDonald et al., 2013) is an international project to develop crosslinguistically consistent treebank annotation for more than 60 languages. The resources by the
project include ancient languages such as Ancient Greek, Coptic, Old Church Slavonic, andSanskrit. The UD Japanese team are developing an annotated corpus of contemporary written
Japanese (Tanaka et al., 2016). However, they have not developed the corpus for the ancientJapanese language. Constructing the UD compatible syntactic dependency annotation onmodern Japanese enables to research linguistic typology with other languages diachronically.
We developed a dependency treebank for the ʻMeiroku-Zasshiʼ corpus in the(National Institute for Japanese Language and Linguistics 2017). We chose
six samples from the corpus because these samples are annotated for(Japanese basephrase) boundaries, two morpheme units (Short Unit Word and Long Unit Word), andmorphological information.
In the first step, we developed-based dependency relations and coordinate structure
annotation compatible with BCCWJ-DepPara standards (Asahara, 2016). One annotator, who is a
graduate student in modern Japanese, annotated the all six samples. There are threephenomena in the modern period that the guidelines for contemporary written Japanese cannot
cover. One phenomenon is inversion derived from classical Chinese (Figure 1). The second is thepredicative adnominal adjective (Figure 2). In contemporary written Japanese, adnominal
adjectives occur mostly in attributive usage. However, in modern written Japanese, bothpredicative and attributive usages are attested. The last one is concord between adverbs andpostpositions (Figure 3). Note that, in the Japanese syntactic standard, the arc direction is fromdependent to head.

Figure 1: Inversion

Figure 2: Predicative usage of adnominal “所謂”

Figure 3: Adverb concord

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JADH 2017

In the second step, we annotated predicate-argument structures on the same samples.
Three annotators annotated the samples. The annotation standard is compatible with NAIST
Text corpus (Iida et al., 2007) and BCCWJ-PAS. The target predicates (red background) areverbs, adjectives, and predicative nouns. We annotated subject (, green background), direct
object ( , yellow background) and indirect object ( ) for the predicates as in Figure 4. We alsoannotated the zero-pronoun information for the predicate when the subject was not overtly
expressed. One issue in the modern language is functional expressions, which are notpredicative expressions. The other issue is resolution of the zero pronoun. The annotator needs
background information about the samples to resolve whether the subject is the first person,
the second person, or others.

Figure 4: Predicate-argument structure annotation

In the third step, we converted these syntactic annotation standards into UD standard. The word
unit of CHJ is based on the UniDic Short Unit Word. The unit is accepted as the UD standard
for Japanese. We introduced conversion rules for the layers of morphology, dependency
attachment, and dependency label. The morphology layer is based on a POS correspondence
table between UniDic POS tagset of CHJ and UPOS (Universal POS in UD). The syntactic
dependency attachments are determined by Bunsetsu-dependency relations as follows: (a) ahead content word in a modifier Bunsetsu attaches to the head content word in the modifiee
Bunsetsu; (b) non-head content words attach to the head content word in the same Bunsetsu;(c) a head functional word attaches to the head content word in the same Bunsetsu; and (d)non-head functional words attach to the head functional word in the same Bunsetsu. The headinformation of content or functional word is determined by the head rules in a dependency
structure analyser CaboCha. Then, the dependency labels are based on conversion rules by the
POS of the paired words, subtree, and predicate-argument relations. Figure 5 shows theconverted dependency tree in Universal Dependency schema.
Note that the arc direction is from head to dependent in the Universal Dependency community.

Figure 5: An example of Universal Dependency tree for Modern Japanese

We still have some issues regarding coordinate structures which have low affinity to dependency
relations, so, we manually modified such relations. In the poster presentation, we will present theoverview of syntactic annotation procedures, guidelines, and data. In our future work, we willenter these syntactic annotations on ʻKokutei Tokuhonʼ and ʻTaiyo Corpusʼ.

Acknowledgements
This work was supported by JSPS KAKENHI Grants Number 17H00917, 15K12888, and17J03579 and a project of the Center for Corpus Development, NINJAL.

35

JADH 2017

References[1] R. McDonald, J. Nivre, Y. Quirmbach-Brundage, Y. Goldberg, D. Das, K.Ganchev, K. Hall, S.
Petrov, H. Zhang, O. Täckström, C. Bedini, N. B. Castelló, and J. Lee. (2013), “Universal
Dependency Annotation for Multilingual Parsing”, Proceedings of the 51st Annual Meetingof the Association for Computational Linguistics, pages 92- 97.
[2] T. Tanaka, Y. Miyao, M. Asahara, S. Uematsu, H. Kanayama, S. Mori, and Y. Matsumoto. (2016),“Universal Dependency for Japanese”, Proceedings of LREC 2016, Tenth International
Conference on Language Resources and Evaluation, pages 1651-1658.
[3] National Institute for Japanese Language and Linguistics. (2017), "Corpus of HistoricalJapanese" (Version 2017.3, Chunagon Version 2.2.2).
[4] M. Asahara, and Y. Matsumoto. (2016), “BCCWJ-DepPara: A Syntactic Annotation Treebank
on the ̀Balanced Corpus of Contemporary Written Japaneseʼ”. Proceedings of the 12th
Workshop on Asian Language Resources, pages 49-58.
[5] R. Iida, M. Komachi, K. Inui and Y. Matsumoto. (2007), “Annotating a Japanese Text Corpus
with Predicate-Argument and Coreference Relations”, Proceedings of the Linguistic
Annotation Workshop, pages 132-139.

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CBETA Research Platform: A Digital Research
Environment for Studying Chinese Buddhist Literaturein the New Era
Jen-jou Hung (Dharma Drum Institute of Liberal Arts)
In recent years, the emergence of digital humanities from traditional disciplines became ahighlight in the field and received considerable attentions from scholars and specialists. Digital
humanities researches focus on applying the application of current information technology to
explore existing archival materials. The objective is to aid in the effort of humanities researchers
to detect patterns hidden in the vast data set which conventional methodology fail to do, or for
developing new approaches to understand the text.
Digitalization and textual analysis is one of the most sought of disciplines in the field of digital
humanities. It is centered around the theme to digitalized ancient texts and presents them assearchable and computable electronic texts. Other than creating research platforms for reading
purposes or databases for further exploration, through text mining applications based on these
data, information between the lines might be revealed. This may contribute, especially to thestudy of ancient texts at various research levels.
In the field of Chinese ancient texts digitalization, the digitization of Buddhist scriptures has
been regarded as a relatively complete and fruitful collection. The Chinese Buddhist Electronic
Text Association (CBETA) has made the Chinese Electronic Tripi aka Collection widely available
for many years and provided a resourceful platform for the studies on Chinese Buddhist texts.
In order to make these materials even more accessible and to provide advanced digital humanity
tools, a new generation called the CBETA Research Platform (CBETA-RP) was developed by
CBETA together with the Dharma Drum Institute of Liberal Arts.
CBETA-RP (http://cbeta-rp.dila.edu.tw/?lang=en) is the result of this project. It is designed tomeet the research need of scholars with new tools. It is functionally tailored for three specificroles:

Figure 1: Homepage of CBETA Research Platform(1) Integrated Text Reader (http://cbetaonline.dila.edu.tw/en/): It provides a whole new online
reading platform with complete canonical texts and functional tools. Other than the entire
CBETA database, functions such as Full-Text Search, Dictionaries Lookup, Term Usage Statistics,
Integrated Bibliographic Database and Buddhist Studies Authority Databases are inclusive.

37

JADH 2017

Figure 2: User interface of CBETA Online Reader.

Figure 3: Full-Text Search results of CBETA Online Reader.

Figure 4: Term and bibliography search results of CBETA Online Reader.
38

JADH 2017(2) Search & Comparison (http://dev.dila.edu.tw/TaishoConcordance/?lang=en): Data
comparison is the first step in data analysis. Through comparing a large number of digital data,
researchers will be able to locate similarities and differences between data for in-depth studies.
This project proposes to design various comparison tools for easy manipulation andconstructive output, including information on corpus and the structure of scriptures etc.

Figure 5: Search and comparison interface(3) Digital Textual Analysis Toolset: Text-mining is an important technique for text content
exploration. With the aid of digital tools, important information on the textual structure andcontext could be uncovered. This project proposes to develop a series of text mining tools
which would enable the user to fly higher. As this is an on-going effort, the result is yet to bepresented.
In this Post, we will introduce the completed, the developing and the proposals of the CBETA
Research Platform to researchers alike for constructive feedback.

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JADH 2017

Situational Effects on Functional Word Frequencies
within Conversational Sentences in Japanese Novels
Hajime Murai (Future University Hakodate)
Introduction
In order to process story texts automatically utilizing artificial intelligence, it is necessary to
identify relationships between linguistic characteristics and various attributes of those stories.
There are many types of attributes that affect text style (e.g., genre, cultural and historicalsetting, social background, charactersʼ personalities, scene mood…). If relationships between
words in texts and key concepts can be identified, it may become possible to develop algorithms
that interpret stories flexibly, just like a human being does. Moreover, those mechanisms could
also be applied in automatic story generation systems.
Among the various elements of story texts, conversational sentences are a particular challenge
for automatic processing. This is partly because colloquial language often include irregularities
reflecting everyday usage of omissions and idiomatic expressions. Moreover, the way eachcharacter talks is often intentionally differentiated in general story texts [1]; this is a generaltechnique used to help readers understand each characterʼs personality. Generally speaking,readers of novel texts can expect the characteristics of each characterʼs dialogue to convey
information to be conveyed regarding various attributes (e.g., gender, age, temperament, social
status) of the characters that appear in the work based on the characteristics of eachcharacterʼs dialogue. Therefore, even if there is no clue to the speakerʼs identity in descriptive
text, most readers can accurately estimate who is speaking by reading only the conversational
sentences.
Of the many attributes of conversational sentences in story texts, this paper focuses on theirsituational attributes, that is, attributes which indicate the charactersʼ circumstances, like era,region, culture, and public or private context. These situational attributes are characteristic of a
specific scene rather than belonging to individual characters (individually) across contexts.
Because situational attributes donʼt directly contribute to character identification, theirrelationship with the characteristics of words appearing in the text has not been analyzed muchin previous research for Japanese novels. However, situational attributes play a important role increating a desired mood; therefore, their mechanisms should be clarified as well if we are to
realize algorithms that can interpret stories flexibly.

Target Corpus
In order to analyze relationships between attributes and conversational sentences, a taggeddialogue corpus of Japanese novels has been developed [2], based on random sampling ofJapanese novel texts within the Balanced Corpus of Contemporary Written Japanese (BCCWJ)[3]. The Japanese novel texts, from among those classified as no. 913 (novels) in Nippon
Decimal Classification, were extracted from the library-based (sub)corpus in BCCWJ; 100 texts
were selected randomly. Conversational sentences were extracted from selected texts, and weremanually tagged for speaker name, gendar of speaker, occupation of speaker, listener name,and relationship between speaker and listener. In all, 5632 utterances from the 100 Japanesenovels were tagged. By combining utterances with the same speaker and listener, this was
reduced to a data set of 887.
The situational attributes of era, area, and public or private situation were also tagged whenfound in story texts (Tables 1 and 2).

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JADH 2017

Table 1: Situational tags about area and era
Era

Present

Past

Domestic

569

194

Foreign

28

82

Area

Table 2: Situational tags about public or private situation
Family90

Friend82

Workplace

362

Other

353

Function Word Frequency and Usage Patterns
As characteristics of text style, the frequencies of different function words in utterances wereadopted in this paper. Usage patterns of function words are differentiated in order to clarify
speakersʼ personalities in these Japanese novels [1].
In the Japanese language, function words are mainly particles and auxiliary verbs. Therefore, inthis paper, statistical significances for frequencies of particles and auxiliary verbs were analyzed,
through the chi-squared test (p value is less than 0.01, Cramér's V is 0.10). Table 3 shows
particles and auxiliary verbs with highly significant values (p value is less than 0.0001) on the
chi-squared test for four pairs of area and era situational tags (present-domestic, presentforeign, past-domestic, and past-foreign). Because there were many words which showedsignificance, Table 3 describes frequently appearing words only. Table 4 does the same for the
public‒private division (p value is less than 0.01, Cramér's V is 0.14).
Table 3: Statistically significant words by area and era
Significantly more
Present-domestic

,

,

,

Significantly less
,

,

,

,
Present-foreign
Past-domestic,,,

Past-foreign(case particle),
,

,

,

,
,

(case particle),

,

,
,

,

,

,

,

,

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JADH 2017

Table 4: Statistically significant words by public or private division
Significantly more
FamilySignificantly less, ,,,(auxiliary particle)

Friend,Workplace

,
,

,

,

,

,

,

,

(case particle),
,

,
,

,

,
,

,

,

,
,

,

,

(auxiliary particle),
,
,
,

,

In Table 3, particles which adjunctively describe tones such as “Yo”, “Ne”, “Teru”, “Wa” aresignificantly more in Present-Domestic cell. Those particles are conversely significantly less inPast-Domestic cell. With respect to auxiliary verbs, words about politeness “Masu”, “Desu” aremore in present division and are less in past division.
In Table 4, particles which connote pragmatic meaning, such as ,
,
,
,
, and
, are
divided into three types by frequency pattern.
,, andare all significantly high in bothfamily and friend categories; ,, andare unique to the family division; and,, andare unique to the friend division. Meanwhile, auxiliary verbs conveying politeness arecharacteristic of the workplace division, implying that a professional way of speaking inJapanese includes polite phrases but excludes unnecessary connote pragmatic meanings.
Moreover, particles that describe tones can be categorized based on the situations in which
they appear.

Conclusions and Future WorkTo analyze relationships between situational attributes of utterances and speech styles, this data
set of randomly selected conversational sentences from a Japanese novel corpus was tagged
and frequently appearing particles and auxiliary verbs were analyzed statistically. The results
show clearly that era, area, and public‒private differences affect language use in terms ofpoliteness and tones. Further investigation with a larger corpus in future work could pull out
more minute characteristics.
References[1] Satoshi Kinsui, “Virtual Japanese: Mystery of Function Words”, Iwanami Shoten, 2003. (InJapanese)[2] Hajime Murai, “Towards Agent Estimation System for Story Text Based on Agent Vocabulary
Dictionary”, IPSJ Symposium series, Vol. 2016, No. 2, 2016. (In Japanese)[3] Kikuo Maekawa, Makoto Yamazaki, Toshinobu Ogiso, Takehiko Maruyama, Hideki Ogura,
Wakako Kashino, Hanae Koiso, Masaya Yamaguchi, Makiro Tanaka, and Yasuharu Den.
“Balanced Corpus of Contemporary Written Japanese”. Language Resources and Evaluation,
Vol. 48, No. 2, pp. 345-371, 2014.

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Enabling digital humanities research and teaching
through digital library APIs
Donald Sturgeon (Harvard University)
As digital libraries continue to grow in size and scope, their contents present ever increasing
opportunities for use in data mining as well as digital humanities research and teaching. At thesame time, the contents of the largest such libraries tend towards being dynamic rather thanstatic collections of information, changing over time as new materials are added and existingmaterials augmented in various ways. Application Programming Interfaces (APIs) provide
efficient mechanisms by which to access materials from digital libraries for data mining anddigital humanities use, as well as by which to enable the distributed development of related
tools. Here I present a working example of an API developed for the Chinese Text Project digital
library (http://ctext.org) being used to facilitate digital humanities research and teaching, while
also enabling distributed development of related tools without requiring centralizedadministration or coordination.
Firstly, for data-mining, digital humanities teaching and research use, the API facilitates directaccess to textual data and metadata in machine-readable format. In the implementation
described, the API itself consists of a set of documented HTTP endpoints returning structured
data in JSON format. Textual objects are identified and requested by means of stable identifiers,
which can be obtained programmatically through the API itself, as well as manually through thedigital libraryʼs existing public user interface. To further facilitate use of the API by end users,
native modules for several programming environments (currently including Python and
JavaScript) are also provided, wrapping API calls in methods adapted to the specificenvironment. Though not required in order to make use of the API, these native modules greatly
simplify the most common use cases, further abstract details of implementation, and makepossible the creation of programs performing sophisticated operations on arbitrary textual
objects using a few lines of easily understandable code. This has obvious applications in digital
humanities teaching, where simple and efficient access to data in consistent formats is of
considerable importance when covering complex subjects within a limited amount of classroom
or lab time, and also facilitates research use in which the ability to rapidly experiment withdifferent materials as well as prototype and reuse code with minimal effort is also of practicalutility.
Secondly, along with the API itself, the provision of a plugin mechanism allowing the creation ofuser-definable extensions to the libraryʼs online user interface makes possible augmentation ofcore library functionality through the use of external tools in ways that are transparent andintuitive to end users while also not requiring centralized coordination or approval to create or
modify. Plugins consist of user-defined, sharable XML resource descriptions which can beinstalled into individual user accounts; the user interface uses information contained in thesedescriptions ‒ such as link schemas ‒ to send appropriate data such as textual objectreferences to specified external resources, which can then request full-text data, metadata, andother relevant content via API and perform task-specific processing on the requested data. Anyuser can create a new plugin, share it with others, and take responsibility for future updates totheir plugin code, without requiring central approval or coordination.
This technical framework enables a distributed web-based development model in which external
projects can be loosely integrated with the digital library and its user interface, from an end
user perspective being well integrated with the library, while from a technical standpoint being
developed and maintained entirely independently. Currently available applications using thisapproach include simple plugins for basic functionality such as full-text export, the “Text Tools”
plugin for textual analysis, and the “MARKUS” named entity markup interface for historical
Chinese texts developed by Brent Ho and Hilde De Weerdt, as well as a large number of external
online dictionaries. The “Text Tools” plugin provides a range of common text processing services
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JADH 2017and visualization methods, such as n-gram statistics, similarity comparisons of textual materials
based on n-gram shingling, and regular expression search and replace, along with network
graph, word cloud, and chart visualizations; “MARKUS” uses external databases of Chinese
named entities together with a custom interface to mark-up texts for further analysis. Becauseof the standardization of format imposed by the API layer, such plugins have access not only to
structured metadata about texts and editions, but also to structural information about the text
itself, such as data on divisions of texts into individual chapters and paragraphs. For example, inthe case of the “Text Tools” plugin this information can be used by the user to aggregate regularexpression results and perform similarity comparisons by text, by chapter or by paragraph, inthe latter two cases also making possible visualization of results using the integrated network
graphing tool. As these tasks are facilitated by API, tools such as these can be developed andmaintained without requiring knowledge of or access to the digital libraryʼs code base or internal
data structures; from an end user perspective, these plugins do not require technical knowledge
to use, and can be accessed as direct extensions to the primary user interface. This distributedmodel of development has the potential to greatly expand the range of available features anduse cases of this and other digital libraries, by providing a practical separation of concerns ofdata and metadata creation and curation on the one hand, and text mining, markup, visualization,and other tasks on the other, while simultaneously allowing this technical division to remain
largely transparent to a user of these separately maintained and developed tools and platforms.

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JADH 2017

Surviving the storm: The Revival of Edo Cultural
Traditions through Local and Cultural Networking
Kumiko McDowell, Kevin McDowell (University of Oregon Knight Library)In the late 19th century the practice of pasting nosatsu, wood block votive slips, on shrines andtemples, became popular among commoners in Edo. A number of nosatsu-kai, that is groups
for nosastu pasting and exchanging emerged. Each member commissioned a team of artisansincluding a designer, a carver and a printer to create original multi-colored nosatsu slips forexchange gatherings and competed to see who could produce the highest quality of nosatsu.
Nosatsu-kai and nosatsu practice, which have traces of the Edo culture from the previous era
largely declined in the early Meiji period mainly because of changes caused by the Meiji
Restoration, such as Westernization and modernization throughout Japanese politics andsociety, negation of Edo culture and tradition, and haibutsu-kishaku that is a movement toabolish Buddhism and Buddhist temples.
However, in the mid Meiji era, nosatsu-kai revived and gradually expanded. They regularly hosted
nosatsu exchange meetings as well as joint meetings. As a historical and social background of
the nosatsu revival and development in the Meiji era, the following aspects that brought people
re-evaluate and appreciate nostalgic conventional culture from the previous period should beconsidered: a tendency in the middle of the Meiji period, at least, partly to reject Westernculture and look to traditional forms of Japanese art and entertainment. The 300 year
anniversary of Tokugawa Ieyasuʼs founding of Edo was celebrated in 1889, showing that somesegments of Japanese society were harkening back to Edo period culture. In addition, due tothe Sino-Japanese War (1894), nationalistic fervor swept through the nation.
With these background phenomena, more importantly, local and social network enhanced
nosatsu-kai revival. In the Edo period commoners were assigned to live in certain areas
depending on their social class. For example, Nihonbashi was a merchant town and Kanda andFukagawa were artisansʼ neighborhoods. They established local communities within their areas.
In addition, social networks developed through the cultural activities, including poetry groups
such as haikai and kyoka groups. These cultural groups expanded from the upper class to the
middle class with the economic prosperity in the Edo period that allowed commoners to haveaccess to arts or poetry. After the Meiji restoration, some of these traditional networks survived
and more developed since they were from the conventional classes in Japanese society. Amongthese network groups, I suggest that the most influential ones for the nosatsu-kai revival werethese three groups: Kanda and Nihonbashi local areas, Shukokai/antique collectors, andChoyukai (Tatoo lovers group) through examining the members of revived nosatsu-kai anddesigns often seen in nosatsu from the era.
The Kanda and Nihonbashi neighborhoods, areas located in the center of Tokyo, were largely
populated by merchants and artisans during the Meiji and Taisho eras. The Kanda/Nihonbashi
local area network includes two groups: Parishioners of Kanda Myojin and workers at fruit andvegetable markets in Kanda. Kanda Myojin is the local tutelary shrine for the areas. Devoted
parishioners organized two groups called Miyakagi-ko 宮伴講 and Ofusegi-ko 御防講 to serve andprotect Kanda Myojin. There used to be huge fruit and vegetable markets in Kanda since theEdo period. Merchants who did their business in the market usually both owned a shop in thearea also resided there. Therefore, there was a strong sense of community in the Kanda market
area.
Members of the Shukokai (集古会)/antique collector group were interested in collecting old
things including toys, books and prints from the previous period. The Shukokai was establishedin 1896, and the group later divided into sub-groups depending on their types of collections andheld regular meetings to show their collections and exchange ideas. The members came from45

JADH 2017

various social classes including noblemen, intellectuals, politicians, and commoners.
The Choyukai is a group devoted to tattooing and was originally established in Kanda by Iseman
and other people who had tattoos designed by Horiuno ( 彫 宇 之 ).The members joined shrine
festivals including the Kanda Myojin festival and the Fukagawa Hachiman festival, to carryportable shrines.
I selected major members of nosatsu-kai (1902-1920) including Iseman ( いせ万 ) , Takahashito( 高橋藤 ), Konsan ( 紺三 ), Chikabo ( 千加坊 ), Setcho ( 櫛朝 ), Izuaka ( いづ赤 ), Shimayone ( しま米 ),Kiwashichi ( キ 0 七 ), Senrei ( 扇令 ), Tataume ( 田々梅 ), Daigin ( 大銀 ), and Futami ( 二見 ) to show
their networking group and relationship and found that, Iseman was at the center of all three
networks among these nosatsu enthusiasts. He was an owner of a vegetable and fruit wholesale
shop in Kanda, commissioned large numbers of gorgeous exchange nosatsu cards throughout
his life, hosted many sessions of nosatsu-kai meetings. He was an antique collector and was oneof the founders of the Choyukai. He was the key person for the nosatsu-kai network and his
networking played an important role for the revival of nosatsu pasting and exchanging activities
in the Meiji and Taisho periods.
Iʼm thinking of employing digital tools to show the networks of nosatsukai-members andrelationship between them by area and each group. Palladio is the best candidate for now. This
project is the first one to analyze and visually describe the nosatsu-kai members and theircultural and regional relationships in late Meiji and Taisho in terms of leaders. I am also thinking
of applying a digital tool for related projects. First, I will include more nosatsu-kai members fromthe period to examine the possibility of the existence of other networks/relationships. Next, I will
study the influence of Dr. Frederick Starr, who was an American anthropologist and devoted
nosatsu enthusiast, on the development of nosatsu-kai in Taisho era in terms of his networking
in Japan. Moreover, I am thinking about comparing cultural networks in the Edo and Meiji era. I
believe that mapping software can be a great tool to demonstrate how cultural networks consist
and develop.

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JADH 2017

Towards a Conceptual Framework for Superworks
Senan Kiryakos, Shigeo Sugimoto (University of Tsukuba)
Jacob Jett, J. Stephen Downie, Yi-Yun Cheng (University of Illinois at
Urbana-Champaign)Jin Ha Lee (University of Washington)
Introduction & Context
While the investigation of traditional cultural products like text remains the staple of humanities
research, there is a growing group of humanists (both digital and traditional) who are focusing
on more recent, popular culture (pop-culture) artifacts and works (e.g., Kashtan, 2011; Helms,
2015; Losh, 2015; Whitson & Salter, 2015). These pop-culture artifacts are undeniably an
important part of our cultural heritage; recent statistics show that the comics market in the U.S.
alone has reached 1.03 billion dollars (Reid, 2016) and consumer spending in video gameindustry in the U.S. is estimated at 23.5 billion dollars (ESA, 2016). Pop-culture artifacts are an
integral part of todayʼs society and their study by scholars is informative with regards todetermining what kinds of things society values and how artists, authors, and other culturalcontent producers interact with and remark upon those values.
Many of these works break the boundaries of traditional humanities research through being
produced using multiple media formats. Born-digital entities like video games are an example
multimedia that digital humanists studying pop-culture (Rockwell & Mactavish, 2004) engage
with. Serial narratives spanning multiple formats like the(Jett et al., 2017), andentities that spawn multiple, parallel series, such as,, and the, form more complex examples.

Complex Work-like Entities (“Superworks”)
The role of adequately modeling entities in information retrieval (IR) systems that digital
humanists use is well recorded (McCarty, 2004) and efforts like Walshʼs Comic Book Markup
Language (2012) provide a fine-grained, TEI-like approach. But many IR systems are based on
descriptive metadata rather than richly annotated data. This poster seeks to examine theboundaries of this more complex entity via the notion of “superwork” in the pop-culture space,
the justification for which has been noted by others previously (McDonough et al., 2010), as
these entities regularly span numerous series and mediums. We provide an example of,hoping that this approach can help provide an opportunity for humanities scholars to reflect
their views and opinions in the design of a conceptual model for this more complex entity.

as a Superwork Exemplar
Looking at the pop-culture phenomenon
in aggregate, we see a conceptual entity
spanning multiple mediums, such as manga, animated films and TV series, games, etc. Thisumbrella entity called
goes beyond the aspects of the FRBR Work concept that deal
with formats. There is a lack of understanding about what information aggregates made up of
related resources are describing, as there is no entity to represent this level in isolation fromformat-specific information. For instance, consider a selected portion of thein Figure 1 (below).
For serial works in general, sub-works that form parts are related by being parts of wholes,
which are themselves parts in bigger wholes (IFLA 1998, p 29). What is unclear is how thealternate formats (i.e., alternate editions) of the core work relate to the overall series. In theirpaper describing the relationships that exist among video games, Lee et al. (2014) develop the“isRemakeOf” relationship (p 6), which articulates exactly how the novel series and anime film
series in Figure 1 relate to the anime series. However, it is still unclear how the “remakes” arerelated to the sequel and prequel manga.

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JADH 2017

Figure 1: Work Model of portion of the

Figure 2: Uncertain Relationship Model Between [Partial] Serial Works

Figure 2 showcases how this problem expands when an entire ecosystem of linked works isbrought together. From a human perspective, the video games in theare related to theand thebut the nature of the relationship is not easily articulated by a conceptual model and thereby
the series are not easily retrieved together in an IR system setting. Together these three series
entities form a large information aggregate and, the creation of a “superwork” entity that
represents information aggregates like the one in Figure 2 can help solve this issue. Our future
efforts will include closely examining the “superwork” entity concept to better understandproperties that are unique to it.

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References[1] ESA (Entertainment Software Association). (2016). Essential Facts about the Computer andVideo Game Industry. Retrieved from: http://essentialfacts.theesa.com/EssentialFacts-2016.pdf[2] IFLA Study Group on FRBR (IFLA). (1998).
. München: K.G. Saur Verlag.
[3] Jett, J., Humpal, N., Valentine, C., & Lee, J. H. (2017). What is a series, really?
(1), pp 24-36.
[4] Kashton, A. (2011). Forward to the past: Nostalgia for handwriting in
and.
(3). Retrieved from: http://
digitalhumanities.org:8081/dhq/vol/5/3/000098/000098.html[5] Losh, E. (2015). What can the digital humanities learn from feminist game studies?
(2). Retrieved from: http://digitalhumanities.org:8081/dhq/
vol/9/2/000200/000200.html[6] Lee, J. H., Clark, R. I., Sacchi, S., & Jett, J. (2014). Relationships among video games: Existing
standards and new definitions. Paper presented at the, 31
Oct.-5 Nov. 2014, Seattle, WA, USA.
[7] McCarty, W. (2004). Modeling: A study in words and meanings. In S. Schreibman, R. Siemens,& J. Unsworth (eds.). Retrieved from: http://
digitalhumanities.org:3030/companion/view?docId=blackwell/9781405103213/9781405103213.xml&chunk.id=ss1-3-7&toc.depth=1&toc.id=ss1-37&brand=9781405103213̲brand[8] McDonough, J., Kirschenbaum, M., Reside, D., Fraistat, N., & Jerz, D. (2010). Twisty little
passages almost all alike: Applying the FRBR model to a classic computer game.
(2). Retrieved from: http://digitalhumanities.org:8081/dhq/
vol/4/2/000089/000089.html[9] Reid, C. (2016).North American Comics Market Reaches $1.03 Billion. Publishers Weekly.
Retrieved from: http://www.publishersweekly.com/pw/by-topic/industry-news/comics/
article/70897-north-american-comics-graphic-novel-market-reaches-1-03-billion.html[10] Rockwell, G. & Mactavish, A. (2004). Multimedia. In S. Schreibman, R. Siemens, & J.
Unsworth (eds.). Retrieved from: http://digitalhumanities.
org:3030/companion/view?docId=blackwell/9781405103213/9781405103213.xml&chunk.
id=ss1-2-10&toc.depth=1&toc.id=ss1-2-10&brand=9781405103213̲brand[11] Walsh, J. A. (2012). Comic book markup language: An introduction and rationale.
(1). Retrieved from: http://digitalhumanities.org:8081/dhq/
vol/6/1/000117/000117.html[12] Whitson, R. T. & Salter, A. (2015). Introduction: Comics and the digital humanities.
(4). Retrieved from: http://digitalhumanities.org:8081/dhq/
vol/9/4/000210/000210.html

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Prototype of Linked Open Data Model for Tang Poems
Yan Cong, Masao Takaku (University of Tsukuba)
In recent years, linked open data (LOD) technology has emerged, and many cultural institutions
use it for making their information freely available to the public. This paper focuses on
information of Tang poem cultural resources, which were written during the Tang Dynasty of
China from the 7th to the 10th century and proposes a data model along with its prototype forTang poems as LOD. Tang poetry is one of the indispensable information resources in classicalliterature. It has been translated in several languages to provide people with the opportunity to
study and use it. When students are being taught Tang poetry in Japanese, it is necessary todeal with a characteristic style of expressions in Japanese, which is quite different from otherlanguages. Our final goal is to provide an application that consists of an LOD dataset with richsemantics, the fulltext data with various expressions, and a cross-lingual information retrieval
system in Chinese, Japanese and English. Not only can this help people learning Tang poem, but
it can also assist Tang poem teaching and teacher education in different languages.
In this paper, we especially aim to collect and organize information about Tang poems, which areused in textbooks. Moreover, we construct a structured data model which covers Tang poem
works and instances, different content expressions of Tang poems and the relationships with
their textbooks. The metadata of Tang poems includes author name, title, and the four types ofstyles which exposit the Tang poem styles in two features. One feature is the length of line with
5 and 7 syllables, and the other one consists of jueju ( 絶句 ) and lüshi ( 律詩 ).
For the data model, we employ the BIBFRAME data model, a standard bibliographic framework
developed by the Library of Congress. The basic structure of BIBFRAME consists of work andinstance. Tang poems are modeled using two separate models: an original and unique poem isregarded as a work entity, and a publication of a poem (work) is regarded as an instance. For
the properties, we utilize several standard vocabularies such as BIBFRAME, Schema.org, andDublin Core, as well as our own vocabulary for Tang poems.
As a dataset, we used Tang poems included in Japanese textbooks. We collected 374 Tang
poems as instance entities. These instances consisted of 59 unique poems, which were modeled
as work entities written by 25 authors, and all poems were retrieved from 53 textbooks used inJapanese junior high schools and high schools, as of 2016. We named four types of content
expressions as unpunctuated text (hakubun; 白 文 ), punctuated text (kundokubun; 訓 読 文 ),reading text (kakikudashibun; 書き下し文 ), and translation text (honyakubun; 翻訳文 ). Punctuatedtext means text which transcribes Chinese classic text with markup annotation. It is a method ofannotating classical Chinese so that the text can be easily read in Japanese. Not only the Tang
poems, but also most of the Chinese classic texts were transcribed in these styles. Figure 1
shows examples of punctuated and reading expressions of the same Tang poem “Thoughts in a
tranquil night” by Li Bai.
In order to design the LOD dataset, we defined several kinds of URIs for Tang poems as follows:
We identified 59 unique poems as works, and defined a resource URI as https://w3id. org/
tangpoem/ , where , = 1, 2, ..., 59, corresponds to a work identifier. We identified 374 tangpoems as instances, and defined a URI as https://w3id.org/tangpoem/instance/ , where ,=1, 2, ..., 374, symbolizes an instance identifier. The four types of Tang poem styles have been
defined as https://w3id.org/tangpoem/style/, wherecorresponds to thename of styles 5, 7, 絶句 and 律詩 . Furthermore, a Tang poem author is defined as https://w3id.
org/tangpoem/author/, whereis the name of the author. Full text content of a Tang
poem is defined as https://w3id.org/tangpoem/instance/ / content, wheremeans theidentifier for the instance.
Using the LOD vocabulary and the collected datasets, we built a Tang poem data model. In thisstructured model, a Tang poem work is linked to all its instances, and then the instances are
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Figure 1: Comparison between different expressions of a Tang poem[1][2]linked to, as much as possible, related information, including the textbooks or other data. Figure
2 illustrates an excerpt of the structural model for Tang poem 1. In the model, there are several
kinds of relationships in respect to Tang poem 1. Tang poem work 1 is realized as instance 8,
which is contained in a junior high school textbook [2]. Instance 8 has a property
bf:instanceTitle, which is the name of the title used in the textbook, and a property bf:partOf,
which is the relationship between instance 8 and the textbook. A full text content of Tang poem
instance 8 is modeled as the type of reading text, and instance 8 has a property
tangpoem:content, which refers to full text content for the instance, and a property
tangpoem:fulltextType, which means that the full text contentʼs type is that of reading text.

Figure 2: The structured model for tangpoem:1This paper described an approach to model Tang poems in the textbooks of 2016 using LOD,
and discussed the issue of describing a context of Tang poem special expressions in Japanese.
In future research, we will attempt to 1) find an appropriate way to identify and name Tang
poem works with interoperable identifier schema, 2) encode expressions of full text content with
Text Encoding Initiative, and 3) build an application of Tang poems with LOD.

Acknowledgment
This work was partially supported by JSPS KAKENHI Grant Number JP16H02913.

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References[1] Katsumi Togo, et al. 2013. “Koutou Gakkou Kokugo Sougou (高 等 学 校 国 語 総 合 )”, Daiichi
Gakushu Inc. https://w3id.org/jp-textbook/ 高等学校 /2012/ 国総 /326
[2] Junʼya Noji, et al. 2015. “Chuugakou Kokugo 3 (中学校国語 3)”, Gakko Tosho Inc. https://w3id.
org/jp-textbook/ 中学校 /2015/ 国語 /928

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JADH 2017

Extracting the Patterns of Harmonic Features within
Mozartʼs Symphonies and String Quartets
Compositions based on the Notion of Pitch-Class Set
Michiru Hirano, Hilofumi Yamamoto (Tokyo Institute of Technology)
Introduction
The study seeks to demonstrate how the notion of pitch-class set (Forte 1973) is useful for
describing the harmonic features within Mozartʼs symphonies and string quartet compositions.
It has been proposed that there are differences between the scores for orchestral and chambercompositions, that reflect divergences in terms of the numbers of string players (Hickman1981). This is- sue has previously addressed by focusing on Mozartʼs symphonies and string
quartet compositions, particularly in terms of the relationships between parts (Hirano andYamamoto 2016a,b).
In this study, we focus on the set of pitches that appear across a complete part and compare
the two genres in terms of their harmonic features. Although differences in the harmonic
features of orchestral and chamber compositions have been proposed, such that harmony isgenerally simple with occasional dissonances for orchestral pieces while there is greater use of
dissonance and more daring harmonic shifts for chamber compositions (Hickman 1981: Table1), those differences have yet to be demonstrated objectively.
Pitch-class set (pc set) refers to the set of distinct integers that represent the pitch classes(Forte 1973: p.3). We utilize pc sets to summarize the various arrangements of notes on a score
and to represent them as minor patterns. Although pc set is unsuitable for describing thefunctions of chords, unlike classical harmonic theory, it can rep- resent harmonic features tosome extent, but, most of all, its major advantages lie in avoiding researcher subjectivity and inbeing able to automatically process large datasets.

MethodMaterials
We target Mozartʼs 59 initial movements, of which 39 are symphonies and 20 are string quartet
compositions. The scores were con- verted into MusicXML, which is a machine readable format.

Data Structure
The pc set for every measure within the materials was determined as follows (Forte 1973).
1．Each pitch that corresponds to one of 12 distinct pitch classes is replaced by the integers 0
to 11.
2．All the pitches that appear within a measure together form the pc set, with repetitions
eliminated.
3．The pc sets are sorted according to the normal order, based on the least difference
determined by subtracting the first integer from the last.
4．The pc sets are transposed to the prime form, which is the form where the first integer is 0.
Through this procedure, a measure containing a major triad (such as a chord constructed fromC, E and G) would, for in- stance, be represented with the notation of [0, 4, 7], regardless of thepitch of its root.

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Results
Theoretically, there are 351 patterns of pc sets. Of those, Mozart used 270 patterns (77%)
within the initial movements of the analyzed symphonies and string quartet compositions.
We examined the relationship between the length of a work and the number of pc sets usedwithin the work (Fig. 1). Given that the overall correlation coefficient is 0.84, a strong correlation
would appear to exist between length and number of pc sets.

Figure 1: The relationship between the length of a work and the number of pc sets used within
the work. The horizontal axis represents the total number of measures. Circles andtriangles represent symphonies and string quartet compositions, respectively.
However, the cluster of works to the right side of the scatter plot are more randomly dispersethan the larger cluster to the left, and, indeed, the correlation coefficient for those 12 works is-0.03.
Figure 2 shows the increases in the number of pc sets along a time axis for the K.551
symphony K.551 and the K.590 string quartet composition, which are the last examples of theserespective genres. Both symphonies and string quartet compositions are constructed from asonata form, which consists of three sections: (1) exposition, (2) development and (3)recapitulation. Table 1 presents the numbers of new pc sets added across the sonata sectionsof the two example compositions.
Table 1: The numbers of new pc sets added across the sonata sections of the two example
compositions.

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Figure 2: The increases in the number of pc sets along the time axis. The horizontal axis
represents the time series based on the measures.

Discussion
The present results indicate that Mozart did not utilize all the theoretical patterns of pc setsthroughout his symphony and string quartet compositions. Although the number of patterns
employed within a work is highly depend on the length of the work, there is no correlation
between length and number of sets in the case of the 12 works that have more than about 250
measures.
Table 1 indicates that there is a correspondence between the introduction of new patterns andsonata section. It is natural for new patterns to be introduced during the earlier exposition anddevelopment sections. More interestingly, new patterns are also introduced during the final
recapitulation section, which suggests that the recapitulation section is not merely a copy of the
exposition section.
The findings from the present study suggest that pc sets could be useful for future studies that
seek to capture the differences between symphonies and string quartet compositions.

Conclusion
The present study examined the variety of pc sets used by Mozart within his symphonies andstring quartet compositions, and demonstrated that the method is potentially useful forsummarizing musical scores.
References[1] Forte, Allen (1973) The Structure of Atonal Music: Yale University Press.
[2] Hickman, Roger (1981) “The Nascent Viennese String Quartet,” The Musical Quarterly, Vol.
67, No. 2, pp. 193-212, Apr.
[3] Hirano, Michiru and Hilofumi Yamamoto (2016a) “Instrumentation of Mozartʼs Symphonies:
Study for Width and Thick- ness of Musical Parts,” in Proceedings of Jinmoncom 2016, pp.
63‒68.
[4] Hirano, Michiru and Hilofumi Yamamoto (2016b) “Quantitative Analysis for Mozartʼs MusicalStyle: The Performance Form of Eine kleine Nachtmusik.,” in Proceedings of AEARU Young
Researchers International Conference, pp. 47‒50.

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Creating A Work Entity Dataset of Console
Games Using Wikipedia
Tetsuya Mihara, Mistuharu Nagamori,
Shigeo Sugimoto (University of Tsukuba)
Background
Manga, animation, and video games are known as representative new media of Japanese pop
culture. Creations in those different media types have close relationships with each other, e.g.
expressing the same story, or sharing the same universe or characters. These relationships areuseful for users to search them. The authorized dataset of these media and their relationships
is required by not only users but also researcher including those who are interested in digital
humanities for new media. It helps to understand the dynamics of new media creations.
However, reliable resources about relationships of creations are insufficient, as archiving these
media types is a relatively new activity. It is difficult to create the authorized dataset fromscratch since thousands of creations had been already produced and new creations areproducing every day. Media Art Database (Development Version) (MADB) [1] is a database ofmanga, animation, and video game published in Japan, produced by Agency for Cultural Affairs
in Japan as the national authority of these creation. However, MADB is created from records ofpackages of each medium, and it lacks the information about relations between each medium.
On the other hand, the information of these relations is available from some resources createdby users, e.g. Wikipedia. Unfortunately, these resources are not suitable to search and reuse.
In this research, we try to create data of the relationships between package records of manga,
animation, and video games using resources from the Web. This paper presents the method
and an experiment creating Work entities of console games from Wikipedia.

Research Objective
Users of console games need conceptual entities to describe a whole creation or groups ofcreations, similar to other media types such as books. Therefore, this research defines twoentity types for the dataset of console games, "package entity" and "work entity". Package entity
represents a publication recognized by titles and specific game console. Work represents a
concept to embody package entities. This idea is based on FRBR [2], which is a conceptual modelof the bibliographic records to describe entities, relationships, and attributes in online librarycatalogues and bibliographic databases from a userʼs perspective.
We focus on describing three characteristics of console game publication by the relationships
between the Work entity and Package entity.
Porting
Some video games for a certain game console are reproduced for different game consoles. Thisis called “Porting”. Users need the information about porting of the video game when they wantto know whether they can get the edition for the game console they have.
SeriesSome game titles compose a series of the game or belong the same game franchise. These
groups are used frequently to find games.
Transmediation
Some games also have close relationships with other creative works of deferent medium, e.g.
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animation and manga. Users need not only records of game titles but also these related works
because they are helpful to understand the whole stories and universes of the game.

How to Extract Work Entities of Console Games in Japan from Wikipedia
The information about the three characteristics shown above is contained in articles on
Wikipedia about video games. We try to extract work entities of console games in Japan fromarticles in Japanese Wikipedia [3]. The extracting method consists of three steps,
Getting the package resources from the list
Lists of game package are available articles belonging the category "Video game lists byplatform" (「ゲーム機別ゲームタイトル一覧」in Japanese). The lists are ordered by platforms andpublished year. A record in the list contains texts of title and publish date of each video gamepackages.
Finding work-instance relationships from the links to article
Some records contain a link to the article related to the video games. What these linked articles
are written about is various. Sometime it is about gameplay, series, or group of creations byuserʼs needs and length of the article. Some links from different records to a same article.
These links represent that these records are belonging same group of a creation . In otherwords, These links represent work-package relationships about the three characteristics.
Identifying media type and series from the DBpedia
DBpedia is Linked Open Data dataset created from Wikipedia. In DBpedia, the article of
Wikipedia is defined as a RDF resource, and information of the article (links, categories,
templates) is provided as properties of the RDF resource. The RDF resource of DBpedia often
has some properties to describe its media type and group of series. We considered these RDF
resources as the one of work entity described by the article and recognized information abouttransmediation (especially manga and animation) and series from the properties.

Experimental Results of the Extraction
We experimentally extracted 26,003 package resources and 17,966 links to the related
article from "Video game lists by platform” in Wikipedia, and 8,199 work entities is created from17,966 articles except duplication of links. 3,770 works aggregate several package entities,
2,414 works are grouped by series, and 1,736 works have relationships with manga andanimation creation.
References[1] Media Art Database (Development Version), https://mediaarts-db.bunka.go.jp/[2] FRBR, https://www.ifla.org/publications/functional-requirements-for-bibliographic-records[3] Japanese Wikipedia, https://ja.wikipedia.org/wiki/

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Visualizing Narratives of the Atomic Bombings ofHiroshima and Nagasaki
Steven Braun (Northeastern University Libraries), Kelsey Menninga
In the book, Paul Cobley begins by emphasizing the primitive role of storytelling inhuman history, stating “...humans have a compulsion to narrate. Especially after the developmentof the verbal faculty, human beings have constantly told stories, presented events and squeezed
aspects of the world into narrative form. Wherever there are humans there appear to be
narratives.” Indeed, narratives operate at many different levels of our daily lives and take on
many forms. There is written narrative, which is embodied in the many forms of literature andthe novel; oral narrative, represented by the sharing of stories passed down through culturesand generations; and historical narrative, the retelling of historical events through stories spatiotemporally situated in people and places. And then there is a new kind of narrative that has
emerged with particular significance in recent decades: visual narrative, or the retelling of
events and stories through images, including cinema and photography. In this sense, datavisualization ‒ or information design more generally ‒ is another kind of visual narrative, one inwhich the act of retelling is founded upon data and the grammar of that retelling is derived
from points, lines, and planes.
Data visualization is thus a powerful medium for representing the manifold nature of narrative
expressed in other forms. By acknowledging the constructedness of knowledge and its
representation, data visualization invites opportunities to recognize the ways in which theexperiences and identities of both user and designer alike are fundamentally implicated in thecreative and interpretative acts. For this very reason, visualization can thus also operate as a
medium for reconciliation, honoring the complexities of narrative when understood as anintersubjective act between designer and user.
In this project, we seek to explore this role of visualization as reconciliatory medium through thelens of two specific events in history: the dropping of the atomic bombs on Hiroshima andNagasaki, Japan. Focusing on narratives of these events that are presented in history textbooksin the United States and Japan, we wish to explore ways in which we may use visualization torepresent and communicate the manifold retellings of these events, their parallels, and theirdifferences. While similar work has been done previously through the
project at Stanford University (http://aparc.fsi.stanford.edu/research/divided̲
memories̲and̲reconciliation), this research seeks to extend that prior work in two primaryways. First, we endeavor to collect a larger sample of narratives from textbooks in bothcountries, providing a database of information that scholars may use for further research.
Second, we intend to present those data in a visual form, thereby increasing their accessibility
to wider audiences. The hope is that by providing greater access to these narratives, new paths
toward reconciliation between the United States ̶ "thick" reconciliation, driven by the people ̶
can be built. In depicting many different narratives from United States and Japanese textbook
portrayals of these events, we hope to demonstrate that there are many different ways ofobserving the same events in history, each of which may have its own strengths and faults.
In the process, we hope to explore the following questions:・

What does it mean to think of data visualization as narrative, and how does that inform theways in which we engage with visualization as a medium for communication?
・ How can visualization be used to guide critical inquiry around events in history that may behighly emotionally and politically charged in their reception?
・ What can visualization reveal about how these events in history have been differentiallydepicted, and how do those differences affect how we engage with those events onpractical, societal, and cultural levels?

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To address these questions, we will carry out this research in several stages. In the first stage,
we will collect as many different narratives of the dropping of the atomic bombs on Hiroshima
and Nagasaki as possible from different textbooks. In this exploratory phase, we are limiting ouranalysis to textbooks specifically due to the known biases and controversies associated withthem in both America and Asia, making them particularly relevant for historiographic study. For
our purposes, “textbook” refers to any primary source used in history courses, and the temporalscoping of “dropping of the atomic bombs” will be determined by the contextual events
immediately preceding and following the bombings that are depicted in the texts. Importantly, toenhance feasibility, data collection will be limited to sources from the United States and Japan
only, and data may be collected through crowdsourcing efforts that engage the participation of
the general public. In the second stage, Japanese texts will be translated into English, and bothtranslated and English-origin texts will be encoded for visualization. In the final stage, those texts
and data will be visualized and shared via an interactive exhibit over the internet.
As this project is in its early stages of development, this poster will describe a prototypicalapproach to these analyses and possible platforms for data collection, including platforms thatengage the general public, as well as important considerations for further research such as
international copyright law. It will also present prototypes for potential visualizations,,interactive and static alternative representations of linear and circular timelines that enablecross-comparison of multiple textbook narratives. As this research involves the collection andstudy of highly unstructured textual data (textbook content), this poster will also discuss
pitfalls and considerations in working with such data for the purposes of visualization.

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JADH 2017

Deep Features for Image Classification and Image
Similarity Perception
Zhenao Wei, Lilang Xiong, Kazuki Mori, Tung Duc Nguyen, Tomohiro
Harada, Ruck Thawonmas, Keiko Suzuki, Masaaki Kidachi(Ritsumeikan University)- This paper describes a study that examines deep features in image
classification and image similarity perception. This work is inspired by previous findings inimage style classification that correlation between a deep-learning networkʼs featuremaps can effectively describe image features for classification. Both objective andsubjective experiments are conducted to evaluate existing methods that derive a feature
vector for a given image from feature maps in a deep learning network.

Introduction
In this paper, we introduce our research achievements in extracting deep features of imagesby using a convolutional neural network called VGG-19 [1]. The effectiveness of extracted deep
features are evaluated on two tasks, image classification and similarity perception, which
underlie our final goal of building a recommendation system forimages in a database1 of
the Art Research Center (ARC), Ritsumeikan University. In particular, we focus on the mostpopular 5 genres:, and(prints of actors,
beauties, landscapes, and stories, and copperplate prints).

Previous Work
Chu and Wu [2] and Matsuo and Yanai [3] independently reported that VGG-19ʼs conv5̲1
layer, having 512 feature maps, is the most effective layer in image style classification. In Chu
and Wu, many types of feature vectors were investigated, but, due to limited space, three ofthem are described as follows:(1) Gram vector: This vector is constructed by the diagonal elements and their lower-partelements in the Gram matrix of conv5̲1. Each element, out of 131328 (512 (512 + 1)/2)
elements, is the dot product of the corresponding pair of feature maps.
(2) Cosine-sim vector: Rather than the dot product, cosine similarity is computed.
(3) Gram-dot-cosine vector: This vector is the result of element-wise multiplying the two vectors
above.
According to Chu and Wu, the Gram-dot-cosine vector is the most effective among all feature
vectors that they examined.
Matsuo and Yanai reported in their work that the following feature vector from conv5̲1 was
the most effective.
(4) Gram-sgnsqrt vector: This vector is the same as the Gram vector, but with each element
being L2 normalized with signed square root.

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JADH 2017

The number of dimensions of each of the four feature vectors above was then reduced, e.g.,
to 4096, using principal components analysis (PCA) in their experiments.
Table 1: Classification results of ukiyo-e classification by SVM
Feature Vector

Gram

Cosine-sim.

Gram-dot-cosine

Gram-sgnsqrt

Content

Classification Rate

86.65 %

88.56 %

86.11 %

80.76 %

84.46 %

Figure 1: An example set in the user study

Experiments
We conducted two experiments using 10,000
images randomly sampled from the ARC
database such that each of the five genres equally has 2000 images. The first experiment isimage classification where feature vectors of 4096 dimensions derived first by each of the four
methods and then reduced by PCA are used as input to a support vector machine (SVM). For
reference, we also experimented with another type of feature vector (called Content vector)
resulting from directly applying PCA to a feature vector whose elements are the outputs of allfeature maps in conv5̲1. Five-fold cross-validation was employed in this experiment.
Table 1 shows the classification results of the SVM with each of the five feature vectors as
input. It is interesting to see that, in contrary to previous work, for Ukiyo-e images, the cosinesim vector is the most effective. Since the Gram-sgnsqrt vector and Content vector are theleast effective for Ukiyo-e, we removed them from our user study described below.
In the second experiment, 15 images (three per genre) were randomly selected as queries.
Twenty college students participated in this user study. Two measures were used to find themost similar image to a given query by each of the three remaining features. One is the
Euclidean distance (finding the image with the lowest distance to the query) and the other is the
cosine similarity (finding the image with the highest similarity to the query). To test theparticipantsʼ reliability, two test sets were added whose query image was also used as a
candidate image.
Each participant was shown, in a random order, 15 sets of a query image and its similar images
according to each of the three features, and was then asked to vote the image that she or hethought most similar to the query image (e.g., Fig. 1). In the experiment, resulting images thatare identical to a query image but with different file names were removed and replaced by thenext most similar candidate images. However, we regarded images having the same content butwith different colors as different. Please also note that if resulting similar images are the sameby different features, only one image will be shown; and if it is voted, this vote will be counted
for all corresponding features.
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All participants correctly selected the image identical to the query image in the aforementioned
two test sets, and Table 2 shows the results of this user study where G, C, and GdC denote theGram, Cosine-sim, and Gram-dot-cosine, respectively, with “e” and “c” the Euclid distance andthe cosine similarity. These results also show that the cosine-sim feature outperforms the otherfeatures, in particular, when the cosine similarity is used as the measure.
Table 2: Results of the user study on ukiyo-e similarity perception
Feature-Measure

G-c

C-c

GdC-c

G-e

C-e

GdC-e

# of Votes

78

121

65

54

108

71

Conclusions and Future Work
Our main finding is that forimages, the best feature extraction method, in terms of both
image classification and image similarity perception, is the one that generates a feature vector
whose element is cosine similarity between the corresponding pair of feature maps. As our
future work, we will develop a content-based recommender system forimages based onfeatures extracted by this method.
References[1] L. A. Gatys, A. S. Ecker, and M. Bethge. Image Style Transfer using Convolutional Neural
Networks. CVPR 2016, pp. 2414-2423.
[2] W. T. Chu and Y. L. Wu. Deep Correlation Features for Image Style Classification. MM ʼ16,
pp. 402-406.
[3] S. Matsuo and K. Yanai. CNN-based Style Vector for Style Image Retrieval. ICMRʼ16, pp.
309-312.

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Constructing a Comprehensive Online Platform for
Corpus Studies in Taiwan
Howard Chen (National Taiwan Normal University)
Researchers have paid increasing attention to the application of corpora on studies related toChinese and English literatures, linguistics and teaching. A wide variety of Chinese and English
corpora have also been constructed, including written, spoken, web corpora as well as corpora
for specific purposes. Although there have been many corpus studies in European countries,
there are still few corpus-based studies available in Taiwan [3][4][5]. The lack of corpus-based
language studies might be due to the following reasons: (1) the sizes of various corpora are
getting bigger, thus it is more challenging for language researchers to process the large data;(2) researchers have difficulties finding available corpora for further research; (3) researchers ingeneral are not very familiar with the PC-based corpus analysis tools, and the PC tools were
somewhat limited in their corpus processing capacities; (4) researchers do not have easy accessto some useful corpus processing platforms in which they can upload and analyze their own
corpora.
Based on the urgent needs of language researchers and students in Taiwan, we examinedvarious corpus analysis tools and discovered that a more robust and comprehensive corpus
analysis platform is needed to better help researchers and students in humanities to analyze
large language data more efficiently and easily. With the help of the experts in humanities andcomputer science, this project attempted to build a useful corpus platform to facilitate corpusbased language, literature, and language learning research in Taiwan.
This project developed two corpus analysis platforms. They were based on two well-known
corpus analysis engines- NoSketch Engine and CQPweb [2]. Both tools were web-based freetools and are open for downloading. Also, we further collected many available open source
corpora (BAWE, BASE, Wikipedia, and so forth) and load them to these two corpus platforms.
After the two platforms were built, these corpus tools were made available for various users to
conduct corpus research works.
Researchers and students not only can use the various existing Chinese and English corpora
but also can apply for their research accounts and upload their own corpora. They can further
use the available corpus analysis tools to analyze their data.
To enrich the content of the two corpus platforms, we also applied the method of “web as
corpus” to collect more web corpora in order to improve the corpus resources of theseplatforms [1]. These WAC corpora include various types of written and specialized corpora(Chinese for specific purposes and English for specific purposes).
Moreover, in the future we will further add more functions to our platforms, including keywords
analysis, n-gram extraction and semantic analysis. In addition, we will also collect and analyze
user behaviors based on the logs of these two corpus platforms, such user information can help
us to further improve the services provided in these two sites.
It is expected that the construction of the corpus platforms can be helpful for the researchersand students in studying English literature, linguistics and teaching. We also expect that with thehelp of these websites, humanity researchers in Taiwan can get access to more comprehensive
and powerful corpus analysis platforms to facilitate their data analyses and further enhancetheir research outcomes.
References[1]. Baroni, M., & Bernardini, S. (2004, May). BootCaT: Bootstrapping Corpora and Terms fromthe Web. In LREC.
[2]. Hardie, A. (2012). CQPweb̶combining power, flexibility and usability in a corpus analysis
tool.
,(3), 380-409.
[3]. McEnery, T., & Hardie, A. (2011).
.
Cambridge University Press.
[4]. O'keeffe, A., McCarthy, M., & Carter, R. (2007).

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. Cambridge University Press.
[5]. Reppen, R. (2010).

64. Cambridge University Press.

JADH 2017

Counting the Traditional Japanese Musical Scales:
Analyzing Folk Songs from Chugoku and Kyushu Districts
Akihiro Kawase (Doshisha University)
Introduction
The main purpose of this study is to estimate the structures of traditional Japanese music inorder to embody the concept of Japanese music culture. We apply a scale detection method
based on Seiichi Tokawaʼs scale theory to musical corpora of traditional Japanese folk songs. A
scale is a series of musical notes in ascending or descending order, which is an importantelement for describing the total system (Tonesystem) and capturing the characteristics of themusic culture. A fundamental aspect of musical analysis is to clarify the scale in a musical piece.
In our past research, we implemented a musical scale detection method based on Tokawaʼsscale theory, and applied to 1,794 song pieces of Japanese folk songs from Honshu (Kawase
and Tokosumi 2011), and compared with Tokawaʼs classification results from the 251 songs. It
turned out that the distributions of the two sets of results almost match. This confirms Tokawaʼshypothesis that most Japanese music is thetype, only a small amount of the music is thetype, and thetype exists very rarely, and provided quantitative data to support it (Theterms will be described later).
However, analyzes targeting small areas in Japan were not implemented. In this study, we carryout classification experiment of Japanese musical scales for both Chugoku district (thewesternmost region of Japan s largest island of Honshu) and Kyushu district (geographicallylocated in the southern part of Japan).

Tokawaʼs scale theory
Seiichi Tokawa, a Japanese music theorist, classified the Japanese musical scales into four major
types:( 陽 ),( 陰 ),( 混合 ), and( 琉球 ). Basically, each scale has five pitchheights, thus we may generate five scales (or modes) per type by allocating the tonic
respectively. Table 1 is a list of Tokawaʼs musical scales, which is a transcript from Exploring theJapanese Musical Scale (Tokawa 1990) with partial modifications to pitch height names using
the alphabetic letters A-G instead of Japanese characters. The left-hand side of Table 1 presents
the scales whose pitch height names are allocated in Tokawaʼs arrangement, and the right-handside of Table 1 has the same scales as the left-hand side, but the only difference is that all
tonics are lined up to pitch A for comparison purposes. Since Tokawaʼs theory is not based on
the F. Koizumiʼs tetrachord theory (Koizumi 1958), there is criticism that Tokawaʼs scale theory
cannot capture the characteristics of Japanese music. However, Tokawaʼs scale theory still
provides a versatile system which covers various conventional scale theories under the present
circumstances. Accordingly, we constructed a scale detection algorithm based on Tokawaʼsscale theory (Kawase and Tokosumi 2011), and manipulated it in the Python Environment.

Overview of data
We use the entire musical notes for works included in the “” (Anthology ofJapanese Folk Songs, 1944-1993) from each province in the Chugoku district and Kyushu
district. In order to digitize the Japanese folk song pieces, we generate a sequence of notes by
converting the music score into MusicXML file format. There were 95,154 tones in the sampleof 886 songs for Chugoku district and 76,028 tones in the sample of 896 songs for Kyushu
district. Therefore, the result of the classification in both districts in this study is that the
proportion oftype is higher than that of Tokawa's result, and the remaining three classes are
few.

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Table 1: List of Tokawaʼs musical scales for traditional Japanese music

Detection results
If we compile except for song pieces less than 5 pitch heights, it was confirmed that thefrequency oftype is generally large andtype hardly appears. Calculating thepercentage for each type, the results of the Kyushu district were 84.94% for thetype,
13.00% for thetype, 1.93% for thetype and 0.13% for thetype. In the
Chugoku district, 92.46% for thetype, 5.84% for thetype, 1.70% for thetype and0.00% for thetype.
Although the material used for the summary is different, according to Tokawa's examination(2007), 251 songs included in(Iwanami paperback library,1960) and
(Iwanami paperback library, 1961) wereclassified as follows: 198 songs (78.88%) for thetype, 43 songs (17.13%) for thetype, 7
songs (2.79%) for thetype, and 3 songs (1.20%) for thetype.
In comparison with various theory of Japanese musical scale theory, for the Chugoku district,thescales were 6.08%, thescale ascending order types were 33.70%, thescaleascending order types were 16.06%, thescales were 23.97%, thescale ascendingorder types were 0.61%, thescale descending order types were 3.77%, and no
scaleswere detected. On the other hand, for Kyushu district, thescales were 7.08%, thescaleascending order types were 23.29%, thescale descending order types (in other words thescales) were 20.21%, thescales were 23.68%, thescale ascending order types
were 0.64%, thescale descending order types (in other words
scales) were
8.37%, andscales were 0.13%.
In the future study, we will resolve the scale detection method and assemble a database ofJapanese folk songs to support musical information, including scale information, historicalinformation, and geographic information, in order to promote folk song research from anengineering perspective.

Acknowledgment
This work was mainly supported by the Japanese Society for the Promotion of Science (JSPS)
Grants-in-Aid for Scientific Research (15K21601).
References[1] Kawase, A., and Tokosumi, A. (2011). Scale Detection for Japanese Music Focused on the
Pitch Sequence of Musical Scales. In Proc. 6th Spring Conference of JSKE 2011, 21C-07.

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JADH 2017[2] Koizumi, F. (1958).
[3] The Society for Research in Asian Music (1982).
Tokyo, Ongaku no tomo sha.
[4] Tokawa, S. (1990).

, Ongaku no tomo sha.
,
, Tokyo, Ongaku no tomo sha.

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Development of System to Encourage Data Sharing
and Usage among Process on Historical Study with
Linked Data
Satoru Nakamura (University of Tokyo)
Introduction
Recently, as represented by Open Science or Open Data, the movement to make some data
accessible to various stakeholders is growing in necessity. Linked Data, which is a method toallow sharing and usage of data, has played an important role in the movement. There havebeen many attempts to apply Linked Data to humanities and historical study. CIDOC CRM[1]provides an object-oriented conceptual model that allows researchers and curators to describe
and retrieve the information required in museum activities. Historical Event Markup and Linking
Project (Heml) [2] provides a format to describe historical events, and software which visualizes
those data with timelines and maps. Moreover, Europeana[3] has developed a framework for its
metadata which called Europeana Data Model (EDM), and allows access to resources possessedby more than 3,000 cultural facilities. Kiourt et al. [4] developed a system called "DynaMus" thatenables virtual exhibition. This system uses data from Europeana such as form and size ofresources.
This study aims to the application of this technology into data sharing and usage amongprocess on historical study. We divide historical study into three types of processes. First one is“Management” process, where professionals such as archivists assess, collect, organize,preserve and provide access to historical resources. Second one is “Research” process, where
historians aim to study about the past and find new knowledge based on historical resources.
Third one is “Exhibition” process, where curators share historical resources and researchachievement with the public and community. These processes are common in terms of using
historical resources, but each of them requires different expertise and stakeholders. Therefore,
these are basically conducted independently. We use Linked Data in order to encourage theefficient coordination among those processes.

Proposed System
Fig. 1 shows an overview of proposed system. This system stores data among processes onhistorical study integrally on the web, by describing them as Resource Description Framework(RDF). Each historical resource is represented by URI, and data provided from each process is
described as metadata of the resource. This study calls data achieved from ”Management”
process as “catalog data”, which is basically bibliographic information of historical resources,
such as title and created date. Data achieved from ”Research” process is called “research data”,
which can be defined and accumulated based on research objectives of each historian, such as
research notes and finding. “Exhibition data” represents data used in “Exhibition” process. This
represents information such as explanations of resource or additional information related tothem such as keywords, in order to help audiences to understand contents or background ofresources. In addition, the usage of Web API such as SPARQL, which is a query language of
RDF, enables to develop applications for different stakeholders and processes in historical
study. This approach of “single source and multiple usage” encourages to share and reuse ofdata and to enhance efficient coordination of processes in historical study.

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Figure 1: Overview of developed system

Case Study
We applied proposed approach to a case of historical study with “Yuzuru Hiraga Digital Archive”.
Yuzuru Hiraga was a vice admiral of Japanese Imperial Navy and a president of the University ofTokyo. He left about 5,500 resources including drawings, engineering documents, reports andothers. As for “Management” process in this case, we developed the application which gives
access to those resources on the web. This application uses catalog data with standard schema
such as Dublin Core. As for “Research” process which uses catalog data from “Management”
process, we conducted historical research to confirm the reason of redesign of naval vessels.
The method to manage catalog data and research data integrally enabled not only to analyze
resources quantitatively with the large amount of catalog data but also to classify themaccording to the purpose of the researchers. As a result, the reason of difference in the numberof funnels between sister vessels was confirmed based on primary resources, such as blueprints
and technical reports. As for “Exhibition” process, we developed the application to exhibit thoseresources on the web by using catalog data and research data. This application gives access tohistorical resources not only with catalog data but also from several points of view such as
timeline, map and network. These achievements demonstrated that proposed approach canencourage to share and reuse of data and to enhance efficient coordination of processes inhistorical study.

Conclusion
The system to encourage the efficient coordination among processes in historical study with
Linked Data was proposed. Developed system allowed users to use data from “Management”,“Research” and “Exhibition” process integrally. This enables to develop applications for different
stakeholders and processes. The effectiveness of developed system was demonstrated throughthe case of historical study with “Yuzuru Hiraga Digital Archive”.
References[1] CIDOC CRM. “What is the CIDOC CRM?” accessed June 30, 2017. http://www.cidoc-crm.org.
[2] B. Robertson. Exploring Historical RDF with Heml, Changing the Center of Gravity:
Transforming Classical Studies Through Cyberinfrastructure, vol 3, no 1, 2009.
[3] A. Isaac, B. Haslhofer. Europeana Linked Open Data --data.europeana.eu, Semantic Web, vol 4,
no 3, pp. 291-297, 2013.
[4] C. Kiourt, A. Koutsoudis, G. Pavlidis. DynaMus: A Fully-Dynamic 3D Virtual Museum
Framework, Journal of Cultural Heritage, vol 22, pp. 984-991, 2016.

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Relationships between Flowers in a Word Embedding
Space of Classic Japanese Poetry
Hilofumi Yamamoto (Tokyo Institute of Technology),
Bor Hodoš㶜ek (Osaka University)
Objectives
The present study, which is ongoing work, focuses on exploring the connection between theinformation content of words and their semantic role within classical poetic Japanese.
Word embedding methods such as Word2Vec (Mikolov et al. 2013) and more recent extensions,
including multimodal word distributions in (Chen et al. 2015, Athiwaratkun and Wilson 2017) andricher representations such as doc2vec in (Le and Mikolov 2014), have been shown effective in
extracting semantic knowledge that performs well on analogy, semantic similarity, and entailment
tasks. In this work, we will quantify the relationship between the information content of a word
and its word embedding vector and examine the possibility of using word embedding spaces ofclassical poetic terms to explain the semantic relationships between terms.
First we will examine if word embeddings encode enough semantic information to be able todetermine specific subordinate words via their superordinate concept, represented by a poetic
term. For example, flowers frequently appear in classical poetry and tend to be used withspecific words. Both ka (aroma) and chiru (fall) are used with flowers such as: “hito wa isa,kokoro mo shirazu, furusato wa, hana zo mukashi no, ka ni nioikeru” by Ki no Tsurayuki (theKokinshu ¯, No.42; the state of human / hearts I cannot know and yet / the blossoms of this /familiar village still greet / me with the scent of years past / translated by Rodd and Henkenius(1984)). “hisakata no, hikari nodokeki, haru no hi no, shizugokoro naku, hana no chiru ramu” by
Ki no Tomonori (the Kokinshu ¯, No.84; the air is still and / sun-warmed on this day of spring‒ /why then do cherry / blossoms cascade to the earth / with such restless changeful hearts/
translated by Rodd and Henkenius (1984) as well). If ka (aroma) and chiru (fall) are truly relatingto specific flowers, it should be possible to acquire the specific name of a flower based on theirword embeddings.
Second, we will examine that the residual of A minus a will be nothing if word embeddings arestrong enough to extract the specific name a from its superordinate concept A and when a 㱨 A
is established.

Methods
We use the Hachidaishū, classical Japanese poem anthologies compiled by the order of theEmperors (ca. 905‒1205), comprised of approximately 9,500 poems. Each poem is tokenized bykh (Yamamoto 2007) which divides poem texts into tokens using a classical Japanese dictionary.
In order to examine the notable relationships between ʻkaʼ (fragrance), ʻchiruʼ (fall), we look at
the cosine similarity scores between terms in the word embedding space generated by
Word2Vec. The Word2Vec model was generated using the Word2Vec implementation in gensim
2.0.0 (R㶅eh ˚u㶅rek and Sojka 2010). A 50-dimensional skip-gram model with negative sampling
was used with context window covering the whole poems.

Results
As a result of measuring the cosine distances between ka (fragrance) and other words, also
between chiru (fall) and other words, the list of the former relationship indicates ume (plum) andthe latter indicates sakura (cherry) as their corresponding flowers (Table 1 and 2).
As the flower of summer, we could obtain tachibana (the flower of orange), while after removing
the vector of tachibana, we could not obtain any names of flowers (Table 3).
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Table 1: Top five words similar to ka (fragrance) and the reverse examination of ka by using theterm ume (plum). Each value indicates the cosine similarity between each word pair.

Discussion
The lists clearly shows that ka (fragrance) is related to ume (Mizutani 1983:130). In terms ofchiru (fall), the latter result replicates well-established knowledge in the literature that falling
flowers denote sakura (cherry) and not ume (plum), and it is discernable that sakura (cherry)
relates to chiru (fall), which indicates that people at the time lamented falling sakura (cherry)(Katagiri 1983: 84). We next looked at whether the Word2Vec word embedding creates a vector
space where geometric algebra is possible and vector distances in the space hold certain
semantic meaning. Among summer flowers, the tachibana (the flower of orange) is very famous.
We expect that if we subtract tachibana out from the summer vectors, the resulting space will
be devoid of relationships between natsu (summer) and hana (flower). By calculating with therelational expressions summer + flower and summer + flower ­ tachibana, the operations
conducted by Word2Vec have been shown to reproduce our current understanding of therelationships between flowers and seasons as well as some emotions associated with them. As
shown in Table 3, the summer + flower operation indeed includes tachibana and it should be
noted that the summer + flower ­ tachibana operation did not include any remarkable values
between summer and flower.

Table 2: Top five similar words to chiru (fall) and the reverse examination of chiru by using theterm sakura (cherry). Each value indicates the cosine similarity between each word pair. pn.
means ʻplace nameʼ.

Table 3: Operations relating to natsu (summer), hana (flower), and tachibana (orange).

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JADH 2017Conclusion
We conducted the experiments using approximately 9,500 classical Japanese poem texts inorder to examine the possibilities of extracting subordinate terms from superordinate concepts
based on word embeddings. We found that the model also allows us to extract specificsubordinate words based on the superordinate concept of classical terms such as: when the
distance between two terms such as tachibana (orange) and natsu (summer) is close enough,the superordinate concept A indicates the subordinate concept a. We could therefore verify
that it allows us to extract the concrete name from its superordinate concept.
References[1] Athiwaratkun, Ben and Andrew Gordon Wilson (2017) “Multimodal Word Distributions,”
ArXiv e-prints, April.
[2] Chen, Xinchi, Xipeng Qiu, Jingxiang Jiang, and Xuanjing Huang (2015) “Gaussian Mixture
Embeddings for Multiple Word Prototypes,” CoRR, Vol. abs/1511.06246, URL: http://arxiv.
org/abs/1511.06246.
[3] Katagiri, Yoichi (1983) Utamakura utakotoba jiten (Dictionary of poetic vocabulary), Vol. 35
of Kadokawa shojiten, Tokyo: Kadokawa Shoten.
[4] Le, Quoc V. and Tomas Mikolov (2014) “Distributed Representations of Sentences andDocuments,” CoRR, Vol. abs/1405.4053, URL: http://arxiv.org/abs/1405. 4053.
[5] Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean (2013) “Efficient Estimation ofWord Representations in Vector Space,” CoRR.
[6] Mizutani, Sizuo (1983) Goi (Vocabulary), Vol. 2 of Asakura Nihogo Shin-Kōza, Tokyo, Japan:
Asakura Shoten.
[7] R㶅eh ˚u㶅rek, Radim and Petr Sojka (2010) “Software Framework for Topic Modelling with
Large Corpora,” in Proceedings of the LREC 2010 Workshop on New Challenges for NLP
Frameworks, pp. 45‒50, Valletta, Malta: ELRA, May, http://is.muni. cz/publication/884893/
en.
[8] Rodd, Laurel Rasplica and Mary Catherine Henkenius (1984) Kokinshū - A Collection of
Poems Ancient and Modern, Boston MA USA: Cheng and Tsui Company.
[9] Yamamoto, Hilofumi (2007) “Waka no tame no Hinshi tagu zuke shisutemu / POS tagger forClassical Japanese Poems,” Nihongo no Kenkyu / Studies in the Japanese Language, Vol. 3,
No. 3, pp. 33‒39.

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Mapping Dickensʼs Novels in a Network of Words, Topics,
and Texts: Topic Modelling a Corpus of Classic Fiction
Tomoji Tabata (University of Osaka)
Introduction
Topic modelling is a machine learning method for uncovering hidden semantic structures in a
corpus of texts. Based on a probabilistic inference algorithm called latent Dirichlet allocation(Blei, et al. 2003), the technique makes it possible to identify sets of frequently co-occurringwords, or topics that characterize a text as well as classify texts into groups defined by inferred
sets of strongly associated topics. One of the major advantages topic modelling has overtraditional key-word detection techniques employed in many stylometric or corpus linguistic
studies is that, despite the fact that topic modelling is an unsupervised method for detecting
groups of words that tend to co-occur in a set of texts, generated models do not simply helpclassify texts according to the degree of similarity between texts in terms of topic composition,
but also enable us to visualize complex yet meaningful interrelationships between vocabulary
items, topics, and more importantly, association between topics and texts in the form of network
diagrams or heatmap dendrograms.

Topic Modelling in Digital Humanities
Meeks and Weingart (2012) posit that, “a synecdoche of digital humanities”, topic modelling is“distant reading in the most pure sense” since it is focused on a collection of texts in its entirety
instead of individual documents. The online, Volume 2, Number 1,
features some exemplar applications of topic modelling in the humanities scholarship, beginningwith lucid introductions to topic modelling for the humanities by Blei (2012) and Brett (2012).
The journal issue covers topics such as “topic modelling and figurative language” (Rhody, 2012),“his- tory of literary scholarship” (Goldstone and Underwood, 2012), “historical dynamics”(Schmidt, 2012), and a technical review of MALLET, a machine learning language toolkit (Grahamand Mil- ligan, 2012). The
issue was immediately followed by thejournalʼs special
number on topic modelling (Vol. 41, 6th Issue). The
articles include works on significant
themes in 19th-century literature (Jockers and Mimno, 2013), “how democracy handles terrorist
threats” (Bonilla and Grimmer, 2012), diachronic changes in language usage (McFarland, et al.,2013), cross-national comparison of disciplinary development (Marshall, 2013), among others.
The two journalsʼs coverage illustrates how topic modelling has become an indispensable coreof digital humanities research.

Research Method
The proposed study applies topic modelling to a corpus of major 18th Century and 19th
Century British fiction (Osaka Reference Corpus for HIstorical/Diachronic Stylistics: ORCHIDS)
with a view to analyzing latent semantic structures underlying in the Dickensʼs fiction andmapping Dickens in the network of words, topics, and texts. What is of special interest is that bymeans of this approach it is now possible to shed new light on thematic structures composedby a large number of infrequent words, which would otherwise escape the net of key-word
statistics due to infrequency of occurrence. Pre-processing treatments of the corpus texts werecarried out before running MALLET on the text set: the first stage of pre-processing includes
text segmentation (i.e. all texts have been sliced into a consecutive 2,000 word segments withlast segments shorter than 2,000 words being discarded for consistencyʼs sake) and removal of
all punctuation marks and mark-up tags since the present version of MALLET is not capable ofhandling them. Frequent proper nouns have been added to the list of stop words so that they
do not make it into topics generated from the corpus. The number of topics extracted from thedata set is another major factor that would likely affect outputs as well as interpretation of
topics. Different settings have been tested so that generated topic models have good enoughgranularity, in the sense that the models are sensitive enough to not overlook fine shades of73

JADH 2017

latent semantic patterns in the corpus: the number of topics generated were varied from 15 to
as many as 200.

Results and Discussion
Table 1 lists 50 most prominent topics discovered from the corpus with their constituent key
words. The complex interrelationships between the topic by way of shared key words can only
be visible when the data is projected on to a network diagram as in Figure 1. With regard to the
topics that are situated towards the outskirt of the map (3, 0, 32, 6, 4, 35, 43, 49, 46, 40, etc.),
their interpretation can be comparatively straightforward. The topics which find themselves in
the most densely populated center may defy our scrutiny unless we consult Table 1.

Figure. 1: Network diagram representing interrelationships between topics by way ofconstituent key words

Figure. 2, a network diagram visualizing association between topics and texts, shows authorialclus- ters as well as diachronic shift in topics in the corpus. The 18th Century texts are locatedin the left-hand side of the diagram, while the 19th Century texts lean towards the right. Textual
cluster- ings emerge in an even more marked pattern: when the topic composition details ofeach text are compared using a heatmap diagram with hierarchical cluster analysis as in Figure.
3, three distinct clusters appear. The Dickens cluster is clearly differentiated from other 19th
Century authorsʼs texts and 18th Century fiction. The 19th Century fiction and the 18th Century
texts can be told apart with the exception of Thackerayʼsand, whichfind themselves attached to a small 18th Century cluster consisting of Defoe, Richardson, andSwift. Of special interest with regard to the style of Dickensʼs novels is that Dickensʼs texts arecharacterized by their frequent recourse the following topics: 48) element of suspense, 9)
facial/bodily gestures, 16) child, 31) description, 5) motions, and 45) quotative markers.

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Table 1: List of 50 topics extracted from the Classic Fiction corpus and their key words

JADH 201775

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Figure. 2: Network diagram representing interrelationships between topics by way ofconstituent key words

Figure. 3: Heatmap of 50 topics across 78 texts (with mean weights scaled)

Emerging results from this research are expected to open up a new avenue of inquiry into key
semantic patterns in the ORCHIDS, thereby suggesting a possibility of building a bridge between
findings from machine learning text mining and linguistic stylistics, between distant reading andclose reading, leading to an empirical interplay of insights that can benefit textual analysis.
References[1] Blei,D.M.,Ng,A.andM.Jordan2003.LatentDirichletAllocation.
,3: 993‒1022.
[2] Blei,D.M.(2012a)Topicmodelinganddigitalhumanities,,2(1),2012.
Available at http://journalofdigitalhumanities.org/2-1/topic-modeling-and-digital-humanitiesby-david-m-blei/[3] Blei,D.M.(2012b)ProbabilisticTopicModels.
,55(4):77‒84.
doi:10.1145/2133806.2133826[4] Bonilla, T. and J. Grimmer (2013) Elevated threat levels and decreased expectations: How
democracy handles terrorist threats,, 41 (6): 650‒669.
76

JADH 2017[5] Brett,M.R.(2012)TopicModeling:ABasicIntroduction,,2(1). Available
at http://journalofdigitalhumanities.org/2-1/topic-modeling-a-basic-introduction-by-megan-rbrett/[6] Crymble, A. (2012) Review of Paper Machines, produced by Chris Johnson-Roberson and Jo
Guldi,, 2 (1). Available at http://journalofdigitalhumanities.
org/2-1/review-papermachines-by-adam- crymble/[7] Deerwester, S., Dumais, S., Landauer, T., Furnas, G., and R. Harshman (1990) Indexing by
Latent Semantic Analysis.
, 41 (6):391‒407.
[8] DiMaggio, P., Nag, M., and D. Blei (2013) Exploiting affinities between topic modeling and thesociological perspective on culture: Application to newspaper coverage of U.S. governmentarts,, 41 (6): 570‒606.
[9] Fothergill, R., Cook, P., and T. Baldwin (2016) Evaluating a Topic Modelling Approach to Measuring Corpus Similarity,, 23th-28th May, 2016, Portorož, Slovenia. Available at
http://www.lrec- conf.org/proceedings/lrec2016/pdf/154̲Paper.pdf[10] Goldstone, A. and T. Underwood (2012) What Can Topic Models of PMLA Teach Us About
the History of Literary Scholarship?
, 2 (1). Available at http://
journalofdigitalhumanities.org/2- 1/what-can-topic-models-of-pmla-teach-us-by-tedunderwood-and-andrew-goldstone/[11] Graham, S. and I. Milligan (2012) Review of MALLET, produced by Andrew Kachites
McCallum,, 2 (1). Available at http://journalofdigitalhumanities.
org/2-1/review-mallet-by-ian-milligan- and-shawn-graham/[12] Hofmann,T.(1999)ProbabilisticLatentSemanticAnalysis,, 289‒296.
[13] J a w o r s k a , S . a n d A . N a n d a ( 2 0 1 6 ) D o i n g W e l l b y Ta l k i n g G o o d : ATo p i c M o d e l l i n g AssistedDiscourseStudyof Corporate Social Responsibility,, 2016: 1‒28.
doi: 10.1093/applin/amw014[14] Jockers, M. L. and D. Mimno (2013) Significant themes in 19th-century literatureOriginalResearch Article,, 41 (6): 750‒769.
[15] McFarland, D. A., Ramage, D., Chuang, J., Heer, J., Manning, C. D., and D. Jurafsky (2013)
Differentiating language usage through topic models,, 41 (6): 607‒625.
[16] Manning, C. D. and H. Schütze (1999). Cambridge, MA.: The MIT Press.
[17] Marshall. E. A. (2013) Defining population problems: Using topic models for cross-national
comparison of dis- ciplinary development,, 41 (6): 701‒724.
[18] Meeks,E.andS.B.Weingart(2012)TheDigitalHumanitiesContributiontoTopicModeling,, 2 (1). Available at http://journalofdigitalhumanities.org/2-1/dhcontribution-to-topic-modeling/[19] Miller, I. M. (2013) Rebellion, crime and violence in Qing China, 1722 - 1911: A topic
modeling approach,, 41 (6): 626‒649.
[20] Mohr, John W. and Petko Bogdanov (2013) Introduction̶Topic models: What they are andw h y t h e y m a t t e r, P o e t i c s , 4 1 ( 2 0 1 3 ) , 5 4 5 ̶ 5 6 9 . h t t p : / / d x . d o i . o r g / 1 0 . 1 0 1 6 / j .
poetic.2013.10.001[21] Mohr, J. W., Wagner-Pacifici, R., Breiger, R. L., and P. Bogdanov (2013) Graphing thegrammar of motives in National Security Strategies: Cultural interpretation, automated text
analysis and the drama of global politics,, 41 (6): 670‒700.
[22] M u r a k a m i , A . , T h o m p s o n , P . , H u n s t o n , S . , a n d D . V a j n ( 2 0 1 7 f o r t h c o m i n g )Whatisthiscorpusabout?,, Issue 12.2. Pre-publication copy. Available at http://pureoai.bham.ac.uk/ws/files/25529512/TopicModelPaper̲ACCEPTED̲VERSION.pdf[23] Papadimitriou, C., Raghavan, P., Tamaki, H., and S. Vempala (1998) Latent Semantic
Indexing: A probabilistic analysis,, 159‒168.
doi:10.1145/275487.275505[24] Rhody,L.M.(2012)TopicModelingandFigurativeLanguage,,2(1). Available at http://journalofdigitalhumanities.org/2-1/topic-modeling-and-figurative77

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language-by-lisa-m-rhody/[25] S a l t o n , G . a n d M . M c G i l l ( e d s . ) ( 1 9 8 3 ).
NewYork:McGraw-Hill.
[26] Schmidt, B. M. (2012) Words Alone: Dismantling Topic Models in the Humanities,, 2 (1). Available at http://journalofdigitalhumanities.org/2-1/wordsalone-by-benjamin-m-schmidt/[27] Ta n g h e r l i n i , T. R . a n d P. L e o n a r d ( 2 0 1 3 ) Tr a w l i n g i n t h e S e a o f t h e G r e a t U n r e a d : S u b corpustopicmodelingand Humanities researchOriginal,, 41 (6): 725‒749.
[28] Törnberg, A. and P. Törnberg (2016) Combining CDA and topic modeling: Analyzing
discursive connections between Islamophobia and anti-feminism on an online forum,, Vol. 27 (4): 401‒422.

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Analyzing Features for the Detection of Happy Endingsin German Novels
Fotis Jannidis, Isabella Reger, Albin Zehe, Martin Becker,
Lena Hettinger, Andreas Hotho (University of Würzburg)
Abstract
With regard to a computational representation of literary plot, this paper looks at the use of
sentiment analysis for happy ending detection in German novels. Its focus lies on theinvestigation of previously proposed sentiment features in order to gain insight about therelevance of specific features on the one hand and the implications of their performance on theother hand. Therefore, we study various partitionings of novels, considering the highly variable
concept of "ending". We also show that our approach, even though still rather simple, canpotentially lead to substantial findings relevant to literary studies.

Introduction
Plot is fundamental for the structure of literary works. Methods for the computational
representation of plot or special plot elements would therefore be a great achievement for
digital literary studies. This paper looks at one such element: happy endings. We employ
sentiment analysis for the detection of happy endings, but focus on a qualitative analysis of
specific features and their performance in order to gain deeper insight into the automaticclassification. In addition, we show how the applied method can be used for subsequent
research questions, yielding interesting results with regard to publishing periods of the novels.

Related WorkOne of the first works was on folkloristic tales, done by Mark Finlayson, who created analgorithm capable of detecting events and higher-level abstractions, such as villainy or reward(Finlayson 2012). Reiter et al., again on tales, identify events, their participants and order anduse machine learning methods to find structural similarities across texts (Reiter 2013, Reiter et
al. 2014).
Recently, a significant amount of attention has been paid to sentiment analysis, when Matthew
Jockers proposed emotional arousal as a new “method for detecting plot” (Jockers 2014). He
described his idea to split novels into segments and use those to form plot trajectories (Jockers
2015). Despite general acceptance of the idea to employ sentiment analysis, his use of theFourier Transformation to smooth the resulting plot curves was criticized (Swafford 2015,
Schmidt 2015).
Among other features, Micha Elsner (Elsner 2015) builds plot representations of romantic
novels, again by using sentiment trajectories. He also links such trajectories with specificcharacters and looks at character co-occurrences. To evaluate his approach, he distinguishes
real novels from artificially reordered surrogates with considerable success, showing that his
methods indeed capture certain aspects of plot structure.
In previous work, we used sentiment features to detect happy endings as a major plot elementin German novels, reaching an F1-score of 73% (Zehe et al. 2016).

Corpus and Resources
Our dataset consists of 212 novels in German language mostly from the 19th century1. Eachnovel has been manually annotated as either having a happy ending (50%) or not (50%). The
1

Source: https://textgrid.de/digitale-bibliothek
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relevant information has been obtained from summaries of the Kindler Literary Lexikon Online2
and Wikipedia. If no summary was available, the corresponding parts of the novel have beenread by the annotators.
Sentiment analysis requires a resource which lists sentiment values that human readers typicallyassociate with certain words or phrases in a text. This paper relies on the NRC Sentiment
Lexicon (Mohammad and Turney 2013), which is available in an automatically translated German
version3. A notable feature of this lexicon is that besides specifying binary values (0 or 1) for
negative and positive connotations (2 features) it also categorizes words into 8 basic emotions(anger, fear, disgust, surprise, joy, anticipation, trust and sadness), see Table 1 for an example.
We add another value (the polarity) by subtracting the negative from the positive value (e.g. aword with a positive value of 0 and a negative value of 1 has a polarity value of -1). The polarity
serves as an overall sentiment score, which results in 11 features.
Table 1: Example entries from the NRC Sentiment Lexicon

Experiments
The goal of this paper is to investigate features that have been used for the detection of happy
endings in novels in order to gain insight about the relevance of specific feature sets on the one
hand and the implications of their performance on the other hand. To that end, we adopt thefeatures and methods presented in Zehe et al. (2016). The parameters of the linear SVM andthe partitioning into 75 segments are also adopted from this paper.
Since reliable chapter annotations were not available, each novel has been split into
75 equally sized blocks, called. For each lemmatized word, we look up the 11
sentiment values (including polarity, see above). Then, for each segment, we calculate therespective averages, resulting in 11 scores per segment. We group those 11 scores into one
feature set.
. As our corpus consists of an equal number of novels with andwithout happy ending, the random baseline as well the majority vote baseline amount to 50%
classification accuracy. Since we assumed that the relevant information for identifying happy
endings can be found at the end of a novel, we first used the sentiment scores of the final
segment ( ) as the
only feature set, reaching an F1-score of 67%. Following the intuition that not only the last
segment by itself, but also its relation to the rest of the novel are meaningful for theclassification, we introduced the notion of: the last segment of a novel constitutes the, whereas the remaining segments belong to the. Averages were also
2
3

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www.kll-online.de
http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm

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calculated for the sections by taking the mean of each feature over all segments in the section.
To further emphasize the relation between these sections, we added the differences between
the sentiment scores of the final section and the average sentiment scores over all segments inthe main section. However, this change did not influence the results. This led us to believe that
our notion of an “ending” was not accurate enough, as the number of segments for each novel
and therefore the boundaries of the final segment have been chosen rather arbitrarily. To
approach this issue, we varied the partitioning into main and final section so that the final
section can contain more than just the last segment.

Figure 1 : Classification F1-score for different partitionings into main and final section. The
dashed line represents a random baseline, the dotted line shows where the maximum F1-scoreis reached.
Figure 1 shows that classification accuracy improves when at least 75% of the segments are in
the main section and reaches a peak at about 95% (this means 4 segments in the final section
and 71 segments in the main section, for a total of 75 segments). With this partitioning strategy,
we improve the F1-score to 68% using only the feature set for the final section () and reachan F1-score of 69% when also including the differences to the average sentiment scores of themain section ().
Since adding the relation between the main section and the final section improved our results inthe previous setting, we tried to model the development of the sentiments towards the end of
the novel in a more profound way. For example, a catastrophic event might happen shortly
before the end of a novel and finally be resolved in a happy ending. To capture this intuition, we
introduced one more section, namely thewhich focuses on the segments rightthe final section, and used the difference between the feature sets for the late-main and).
the final section as an additional feature set (Using those three feature sets, the classification of happy endings reaches an F1-score of 70%
and increases to 73% when including the feature set for the final segment.

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Table 2: Classification F1-score for the different feature sets

Table 2 summarizes these results and shows that the addition of each feature set leads to small
improvements, amounting up to a F1-score of 73%. While we saw that the classification
performs best when the final section consists of 4 segments, we also observed that quite a few
novels could be correctly classified with several different partitionings. On the other hand, somenovels could not be predicted correctly with any choice of partitioning. An example isby Jules Verne which evidently has a happy ending with clearly
identifiable boundaries, but an extremely short one, consisting only of about 250 words. Theseobservations show that the notion of a novelʼs “ending” is highly variable and can differ
considerably from text to text.
This raises the question whether we can use the sensitivity of
our approach to this kind of variability in order to better understand the characteristics of thenovels in our corpus. As an example, we studied whether different section partitionings are in
any way correlated with the publication date of a novel. In order to keep the results asinterpretable as possible, we focused on one single feature set: the sentiment scores of the final
section. In a first attempt, we divided our corpus into four subgroups, distinguishing novels
published before 1830 (65 novels), between 1831 and 1848 (31 novels), between 1849 and1870 (29 novels) and after 1871 (87 novels). This split resulted in similarly sized portions anddid not yield a strong bias towards happy/unhappy endings in any period.
Figure 2 shows that the best classification is again obtained when about 95 - 98% of thesegments are in the main section, regardless of the time period. Therefore, the best section split

Figure 2 : Classification F1-score for different partitionings into main and final section. Eachline denotes novels from a different time period. The dashed line represents the randombaseline for the time period starting from 1871. Random baselines for the other periods yieldslightly worse results and are omitted. The dotted lines show where the maximum F1-score isreached for the respective time periods.
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point is not correlated with the publication date of a novel. What is striking, however, is the factthat the novels published after 1848 yield considerably lower scores than the novels published
before that year, mostly even below the baseline. This indicates a correlation between
publication date and automatic classification quality, i.e. novels published before the period ofRealism are more easily classifiable in terms of having a happy ending than realistic novels. A
possible explanation is that many novels of that earlier period are more schematicallystructured.
We are aware that the number of novels for each of the time spans is rather small, so that thosefindings can only be regarded as exploratory insights. Nevertheless, these preliminary results
show that the automatic detection of happy endings, even with only one rather simple featureset, can uncover dependencies to other properties of novels that are highly interesting forliterary studies.

Conclusion and Future Work
The automatic detection of happy endings as a major plot element of novels is a valuable step
towards a comprehensive computational representation of literary plot. Our experiments showthat different features based on sentiment analysis can predict happy endings in novels withvarying but reasonable quality. Even though our approach is still rather simple, we showed thatit can potentially lead to substantial insights for literary scholars.
Future work may cover improving our classification by accounting for the high variability ofendings in novels and may also include further leveraging our approach to study thecharacteristics of different novel collections in-depth.
References[1] Elsner, Micha (2015): “Abstract Representations of Plot Structure”, in:
12 (5).
[2] Finlayson, Mark A. (2012):. PhD
thesis, Massachusetts Institute of Technology.
[3] Jockers, Matthew L. (2014): “A novel method for detecting plot”. http://www.matthewjockers.
net/2014/06/05/a-novel-method-for-detecting-plot/ [Access date 25. August 2016].
[4] Jockers, Matthew L. (2015): “The rest of the story”. http://www.matthewjockers.
net/2015/02/25/the-rest-of-the-story/ [Access date 25. August 2016].
[5] Mohammad, Saif / Turney, Peter (2013): “Crowdsourcing a Word-Emotion Association
Lexicon”, in:
29 (3): 436-465.
[6] Reiter, Nils (2013):. PhD thesis, Heidelberg University.
[7] Reiter, Nils / Frank, Anette / Hellwig, Oliver (2014): “An NLP-based Cross-Document
Approach to Narrative Structure Discovery”, in:
29 (4):
583‒605. 10.1093/llc/fqu055.
[8] Schmidt, Benjamin M. (2015): “Commodius vici of recirculation: the real problem with
Syuzhet”. http://benschmidt.org/2015/04/03/commodius-vici-of-recirculation-the-realproblem-with-syuz het/ [Access date 25. August 2016].
[9] Swafford, Annie (2015): “Problems with the Syuzhet Package”. https://annieswafford.
wordpress.com/2015/03/02/syuzhet/ [Access date 25. August 2016].
[10] Zehe, Albin / Becker, Martin / Hettinger, Lena / Hotho, Andreas / Reger, Isabella / Jannidis,
Fotis (2016): "Prediction of Happy Endings in German Novels", in:.

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Machine-Learning Approaches to Literary Works:
Novels of Sir Arthur Conan Doyle
Ayaka Kuroda (University of Osaka)
Sir Arthur Conan Doyle is well-known as the author of Sherlock Holmes series, and a number ofstudies have been carried out to examine charactersʼ personalities or investigatory details in theHolmes series. However, his strong inclination for historical fiction has not widely beenrecognized. To clarify his worksʼ features, it is important for us to focus not only on Holmes
series but also on novels of other genres. Also, we have to pay attention to stylistic aspects of
his novels and short stories. In the humanities, quantitative investigation using computers is
attracting attention, because it is believed to be able to complement existing literary studies
from a new point of view. The work by Burrows (1987), who investigates the works of Jane
Austen using Principal Components Analysis (PCA), was one of the pioneering studies whichobtain great benefit from literary computing. Such a stylometric approach falls within a practiceof ʻdistant readingʼ (Morretti, 2000), which is an antonym of ʻclose readingʼ.
This study attempts to find differences between Doyleʼs historical fiction and detective fiction
through statistical analysis of words in texts with special reference to word frequency pat- terns,
and provides a new perspective for literary scholarship. Two types of machine-learning analyses
are conducted to highlight linguistic or stylistic features that distinguish the two text genres.
First, we used Random Forests to find genre-specific ʻkey wordsʼ, which are significantly more
frequent in one category than in another. In corpus linguistics, several studies have pro- posed
various measures for identifying key words in the form of significance testing, such as loglikelihood ratio and chi-square value. These methods, however, can be inadequate when the
corpus/corpora under investigation include(s) long novels, because proper nouns or words
related to particular themes/settings tend to get an excessively high keyness score. To deal withthis problem, Tabata (2015) proposed a method that uses Random Forests, an ensemble
learning algorithm for classification or regression, to extract words contributing to classification.
In this study, all texts were automatically classified into two groups according to word frequency
patterns with an accuracy of 96.58%.
Table 1: Random Forests result summary

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Figure 1: Relationships between topics and their constituent words (50 topics extracted)
The above Figure 1 is a 3D scatter plot which is generated based on Multi Dimensional Scaling(MDS). According to this figure and Table 1, small number of the Sherlock Holmes text
segments (five out of forty-nine 10,000-word blocks) were misclassified as historical fiction. We
examined these segments to account for the reason why they were mislabeled as historicalfiction. Two out of the five misclassified blocks belong to Part II of, the partrecounted in the style of a ʻretrospective narrativeʼ. Part II of the novel explains past events andthe background of the murder, while Part I describes Holmesʼs investigation and his great
success. The same can be said of other three segments: those sections also accounts for thecauses of the case in ʻretrospective narrativeʼ. We therefore conclude that these retrospective
narrative has similar stylistic features to historical fiction. To make these features more specific,
we made a list of words which contributed to the classification. Based on the list, we foundsome key words of historical fiction: ʻcriedʼ and body-part words such as ʻheadʼ, ʻfacesʼ, ʻarmsʼ
and ʻeyesʼ. Key words of detective fiction, on the other hand, include ʻcaseʼ, ʻfoundʼ, or words
related to housing and furniture such as ʻhouseʼ, ʻdoorʼ and ʻchairʼ.
Second, MALLET was used in conjunction to build topic models based on Latent Dirichlet
allocation (LDA) proposed by Blei, Ng and Jordan (2003). Topic modeling is one of text mining
tools using machine-learning algorithm for discovering ʻlatent topicsʼ of texts, a cluster of cooccurring words. By intuition, readers infer the topic of a text by its word-usage pattern: if atext includes words ʻgameʼ, ʻplayersʼ, ʻballʼ and ʻgoalʼ, readers presume that the text is aboutʻsportsʼ. Also, we can unconsciously understand that one text may contain two or more topics,
such as ʻsports and medicineʼ or ʻsports and economyʼ. Topic modeling simulates this intuitionby mathematical calculation of what words constitute a topic and to what extent a text exhibitsa particular topic(s). That is to say, Random Forests could find some ʻkeywordsʼ, but topic
modeling can reveal not only keywords but also relationships between them.
The granularity of topic models depends on the number of topics extracted: with a very small
number of topics, generated models may end up being too coarse to interpret, while with a verylarge number of topics, resulting models will likely include insufficient number of items, too few
words to observe their complicated relationships from. The ideal setting is depending on thetarget corporaʼs scale or the purpose of modeling, so there is no optimal number.

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In this study, we experimented with two different settings: we compared results obtained from
20 topics and 50 topics. Network graphs were drawn to show complex relationships be- tween
topics, those between topics and their constituent words, and association between topics andtexts. We found some topics which emerge more frequently in detective fictions. The ʻHousing
and Furnituresʼ topic was among the most important features of detective fiction, a result thatis in keeping with findings obtained with Random Forests. A topic labeled as ʻCharacters ofHolmes Series and Criminal Investigationʼ was also one of the key topics of detective fiction. It
includes ʻHolmesʼ, ʻWatsonʼ, ʻcaseʼ, ʻinspectorʼ and ʻpoliceʼ, but character names and common
nouns are mixed in one topic when 20 topics were extracted. When we built 50 topics, however,they were divided into two topics and became independent of each other, so the co-occurrenceof each topics became clearer.
We used two algorithms, Random Forests and topic modeling, to apply quantitative analysis toConan Doyleʼs historical fiction and detective fiction. What emerges from our investigation are
linguistic features that differentiate between these two text genres. Since the linguistics
features of his novels have hardly been discussed in the existing literature, we hope to be able
to provide a new perspective for literary investigation into the novels of Sir Arthur Conan Doyle.

Figure 2: Relationships between topics and their constituent words (20 topics extracted)

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Figure 3: Relationships between topics and their constituent words (50 topics extracted)

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Human-assisted OCR of Japanese Books with
Multistep Microtasks
Kosetsu Ikeda (Chiba University),
Kiyonori Nagasaki (International Institute for Digital Humanities),
Atsuyuki Morishima (University of Tsukuba)
In Recent years, digital transcription for Japanese books with crowdsourcing become popular inJapan. Because the Japanese language contains thousands of characters some of which aresimilar to each other, digital transcription needs much human power even if the transcribing
objects are printing with type. There are two common approaches to transcribe. One is theusing online collaboration tools for specific manuscripts, articles, books, and so on. The other isthe human-assisted optical character recognition (human-assisted OCR) approach, where crowd
workers correct the results of OCR software or directly transcribe characters that the OCR fails
to transcribe. The human-assisted OCR approach is compatible with micro-volunteering, that is,
to divide a big task into small ones to be able to be solved by crowd workers. It will enable totranscribe in parallel and minimize individual burden.
This paper reports our project for transcribing the book collections of National Diet Library(NDL) in Human-assisted OCR approach with Crowd4U, a micro-volunteering and crowdsourcing
platform.
Our project is unique in three ways. First, we divide digital transcription into multistep microtasks
to reduce a requestorʼs burden and improve output quality. In the first step, crowd workers
assess a part of the object as to whether it should transcribe. This step try to exclude
disturbance like figures, tables, and so on from following steps. In the second step, crowd
workers judge whether some OCR results are correct or not at once. Because to correct OCR
results for every single character is inefficient, crowd workers make a rough judge in this step.
This approach is effective if OCR boast high precision. In the last step, crowd workers correct
OCR results as every single character. In all steps, we adopt using majority votes to absorb
some workerʼs mistake.
Second, we try to extend the human-assisted OCR approach by distributing microtasks in many
ways other than just showing tasks on PC screens. Most of existing projects ask crowd workers
to perform such tasks on the specific Web page. In contrast, we try to extend the approach by
distributing tasks in many other ways. For example, we distribute tasks to the task-on-the-floor(shortly TOF) system, where people walking perform tasks while walking on the floor, and to thesmartphone lockscreen application, where people who want to unlock their smartphones
perform tasks. These are very useful for simple tasks, like the first step and the third step tasks.
It helps to keep the number of performed microtasks stable.
Third, we deal with Japanese books which have thousands of characters. This raises aninteresting question because people performing microtasks may not pay a great attention to
the task compared to the case where they perform tasks on the specific Web page.
In Crowd4U, if the workers logging in, their names will appear in the list of contributors on our
web site. Also, they can leave themselves anonymous.
The main contributions are as follows. First, we explain our ongoing project whose approach isnovel. To the best of our knowledge, our project is the first to try to transcribe with multistep
microtasks and distribute tasks in many ways other than PC screens in the digital librarydomains. Second, we show our preliminary results that suggest that our approach is effective tosome extent although the Japanese language contains thousands of characters some of which
are similar to each other.

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Creating Geotagged Humanities Data via Mobile Phone:
Opportunities and Challenges
David Joseph Wrisley (New York University Abu Dhabi) Mario Hawat,
Dalal Rahme (American University of Beirut)
Discussions of crowdsourcing in the digital humanities often concern textual data, in particular,transcription of manuscript data. Theorists point to the ability of “groups to out-perform
individual experts, [and] outsiders [to] bring fresh insights to internal problems” (Brabham). In
terms of data collection, mobile devices are described as freeing humans to collect significantly
larger samples of data in space, in particular in the domains of citizen participation or
infrastructure management. Crowdsourced data collection via mobile devices might seem likean ideal match, and yet the technique presents numerous challenges, foregrounding thenecessity for digital humanities research to be vigilant about the changing modes of cultural
knowledge production in “complex computational societies” (Berry/Fagerjord). Our paper
discusses three ways that collecting data for digital humanities projects via mobile phones
introduces new levels of data complexity: (1) the tension between ontological precision and on
the fly human-intelligence tasking (2) the paradoxes of urban mapping research: redundancy,
coverage and privacy (3) the impact of human behavior with socially embodied devices insensitive environments.
Our paper discusses the experience of the “Linguistic Landscapes of Beirut” project (llbeirut.
org). Our data consist of geotagged photos captured via mobile phones. The 2000+ images ofurban multilingual writing were collected in two phases over two semesters in 2015- 16 year bya team of some thirty undergraduate researchers. They include metadata that are bothautomatically generated (time, latitude, longitude, image-size and phone model) and usergenerated (language, script and linguistic features). A third post-processing phase of the project
began in late 2016 focusing on more granular annotation and the transcription of themultilingual text found in the photos in YAML format.
Our project corresponds to two aspects of Brabhamʼs taxonomy of crowd participation:knowledge discovery and distributed human-intelligence tasking. Compared to other mobile
application-based linguistic landscaping, our project is smaller in scale, yet richer in metadata(Lingscape). Whereas our data resemble volunteered geographical information (VGI), in thatdata collectors were free to capture images anywhere within an urban perimeter, it might bebetter described as a semi-directed, collaborative mapping since choice on the app data form
was constrained to a bounded set of fields dictated by specific research questions.
First, an issue often discussed with VGI is the resultant data quality. Indeed, in the postprocessing phase some inconsistencies in the classification of the samples were uncovered, but
a more salient issue in the social creation of data was what we might call an ontological “shift,”whereby the crowd avoided some categories and moved to nuance them in open comment
fields. This was partially solved by iterative analysis of the data and collective reassessment ofdata fields. By keeping the number of “on the go” human-intelligence tasks to a minimum in thedata entry form, we believed to be assuring data quality, but there were still a number of
unclassifiable examples. We have attempted to deal with ambiguity in the post-processing phase
via image annotation in order to qualify and identify these samples.
Second, whereas it has been argued that the geographical information from historical sources isinherently ambiguous and reflects user bias (Dossin et al.), the geolocation in our project was an
automatic feature of the form builder. The data collection was left intentionally unstructured-participants were not obliged to use a specific sampling method. On the one hand, this meantthat data accumulated along routes of the teamʼs daily mobility. Again, iterative analysis of thedata during the collection process showed the zones of greatest data density. Seeing thisvisualized in real time in-app and in web mapping environments encouraged some to venture
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out into uncharted spaces in the city. On this point, we would argue that user specificity of thedata reflected less a bias than contributing to the overall diversity of neighborhood coverage.
This, however, required the anonymization of the dataset, since participants tended to collectdata around their places of work and residence.
Third, discussions of VGI often mention user motivation. We did notice that although some“super users” (Causer and Wallace) emerged in the initial phases of the data collection, thisabated, perhaps due to the social pressure not to over-perform in the pedagogical setting.
Although pacing the data collection out over time was encouraged, the numbers of image
samples captured tended to intensify at the end of each academic term as shown in Figures 1
and 2. We do not believe this to be a problem for the linguistic landscape data, but could havean impact on other projects that are more time sensitive.

Figures 1 and 2: A bar chart representing the number of data points collected per day over
the two phases of data collection, with spikes at the end of each semester.

The mobile application method of the data collection “on the go” did allow us to scale up thedata rather quickly, but it also revealed a cultural discomfort with photography in a divided citywith highly visible security mechanisms. On the one hand, we realized that many of the photos
were being taken out of car windows, a phenomenon for which the automatically-generated
GPS̲SPEED field provided some insight (Hawat). A more astonishing crowd pattern emerged
however when we examined the aggregate of the data against the visible security mechanisms
in municipal Beirut and found an uncanny avoidance of almost all of the secured zones of the
city.

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JADH 2017

Figure 3: A map showing the aggregate of the “Linguistic Landscapes of Beirut” data (red)along with a rough depiction of the visible security mechanisms (yellow) following Harb et al.

This leads us to question what other blind spots exist in human behavior with mobile devices
and encourages us to rethink the assumption of full freedom of movement through urban spacewhen it comes to data collection.

Works Cited (all links accessed on 30 June 2017)[1] Berry, D. and Fagerjord, A. (2017).
, Polity.
[2] Brabham, D. (2013).
, MIT Press.
[3] Causer, T. and Wallace, V. (2012). Building A Volunteer Community: Results and Findings
from Transcribe Bentham.
, vol. 6.2 http://www.digitalhumanities.org/dhq/
vol/6/2/000125/000125.html[4] Dossin, C., N. Ningning Kong and B. Joyeux-Prunel. (2015). Applying VGI to collaborative
research in the humanities: the case of ARTL@S,. http://dx.doi.org/10.1080/15230406.2016.1216804[5] Gilles, P. And Purschke, C. (2017). Lingscape. Mobile Application. https://lingscape.uni.lu/[6] Hawat, M. (2016). “Mapping Mobility.” Blog. http://www.mariohawat.com/blog/2016/5/16/
moving- magic?p[7] Harb, M., Gharbieh, A. and Fawaz, M., eds. (2009).
. IABR.
[8] March, J. (2016). “On Choosing a Mobile Platform in the Digital Humanities.” Blog. https://digitalfellows.commons.gc.cuny.edu/2016/02/29/on-choosing-a-mobile-platform-in-thedigital-humanities/

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Another Kind of Mime. Another Kind of Digital Humanities
James Brusuelas (University of Oxford)

As papyrologists, we are part of a long-standing initiative to integrate our discipline into thedigital sphere through websites, algorithmic textual analysis, and general applications based on
visual and textual data, an initiative that goes back to the 1990ʼs. The goal has been to produce
tools that have impact on the research of professional scholars and students. The field of
papyrology has been quite successful in this respect. However, digitally creative and artistic
output aimed at communities beyond academia is under represented in the evolving Digital
Humanities. And so, when the Arts and Humanities Research Council in the UK announced aunique funding challenge to produce creative digital content, we proposed. For Classics, only small scaleattempts at integrating animation have been conducted, such as the Panoply project (www.
panoply.org.uk), which animates static scenes on ancient vases for enriching the teaching andlearning about well known Greek myths. There has been no merging of classics scholars andprofessional artists from the animation industry to bring about a digital transformation ofancient culture that has cultural impact beyond the academic community. Can the artistry ofanimation be integrated with papyrology to visualize and help resurrect lost ancient performative
voices preserved only in fragmented papyri?
In 2014 thepublished P. Oxy. 5189, a 6th century CE Greek Mime. A popularform of entertainment in antiquity, the Mime was a type of sketch comedy performed on stage
or in the street and known for its vulgar humour and improvisation. Despite such popularity, thetextual evidence of ʻlowʼ literature and entertainment of the Graeco-Roman world did not makeit into the familiar channels of the mediaeval transmission, a process in which selected authors
were copied by scribes and scholars. Our knowledge of popular entertainment is thus largely
dependent on papyri. Reconstruction of this fragmentary evidence, however, is a challenge dueto the literal holes and gaps in the text, which we supplement as best we can, according to ourknowledge of the Greek language and literature. In this case, there is only a narrative description
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JADH 2017of the movements and actions of the characters, including the actorsʼ lines, with the following
discernible content: 1) clothing (perhaps cross-dressing); 2) eating and (badly) cooking; and 3)
beating and slapping, perhaps triggered by bad cooking. As a reading text, it is rather awful due
to its fragmented state. But this was never a text to be read. It was meant to be acted. It wasmeant to be seen. Accordingly, the idea of animation as a visual, rather then purely textual,means of reconstruction emerged.
The purpose of this paper is to introduce the animated short film
. In collaboration with Acme Filmworks, a BAFTA and Academy Award winning animation
studio in Los Angeles, CA, papyrology at the University of Oxford engaged in a different kind of
Digital Humanities. Based on the remaining content of P.Oxy. 5189, we experienced the fullproduction process, from script and storyboards to multiple digital animatics and final edits.
Over the course of production we thus addressed a variety of research questions. From both
digitally artistic and academically philological perspectives, what does it mean to digitallytransform a papyrus fragment, its fragmented content, into an animated short film? Who is theaudience? Is it a kind of documentary? Or is it entertainment and thus subject to poetic license
and adaptation? More importantly, why do it all? And when finished, what does one do with it?
Can it be used for teaching? In then end, what place does animation have in Digital Humanities?

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JADH 2017+RQJVX:DQJ +DUYDUG8QLYHUVLW\ 

Matrix and Graph Operations for RelationshipInference: An Illustration with the Kinship Inferencein the China Biographical Database

Chao-Lin Liu (Harvard University/ National Chengchi University),
Hongsu Wang (Harvard University)
Biographical databases contain diverse information about individuals. Person names, birthinformation, career, friends, family and special achievements are some possible items in therecord for an individual. The relationships between individuals, such as kinship and friendship,provide invaluable insights about hidden communities which are not directly recorded indatabases. We show that some simple matrix and graph-based operations1 are effective forinferring relationships among individuals, and illustrate the main ideas with the China
5HODWLRQVKLS0DWULFHV
Biographical Database2 (CBDB).

Relationship Matrices
Assume that we have 7different individuals anddifferent relationships. Let , 㱨 Π={1,…, },represent the individuals, and , 㱨 Λ={1,…, }, represent the relationships. We can store therelationships between theEDVLF
individuals with a matrix , in which a cell T1,2 is the relationshipbetween 1 and 2 that is recorded in a database. In CBDB, an 1 can be basic, e.g., “F” which
H[WHQGHG
represents a “father-of” relationship, and an 2 can be extended, e.g., “DHB” which represents a“daughterʼs husbandʼs brother”3 relationship while the participating individuals in between are
unknown. In the matrix in Figure 1, we have T1,2= “F”, and that means P2 is a father of P1. That
T2,3= “DHB” means P3 is P2ʼs daughterʼs husbandʼs brother.

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Figure 1: A relationship matrix for kinship)LJXUHA relationship matrix like that in Figure 1 does not have to be symmetric. In addition, a cell inthe matrix may need to accommodate multiple values when necessary. When we convert therelationship matrix directly from a biographical database, the original matrix may be asymmetric,
UHODWLRQVKLSPDWUL[
because the information stored in databases are not symmetric. If we wish, we can build asymmetric matrix from a raw matrix like that in Figure 1, by checking and combining T , andT , for any combination of㱨 "Π" and
㱨 "Π" . In this process, we might find conflicting oreven parallel relationships between two individuals, which will need domain experts for further
inspection. A person may have parallel (or multiple) relationships with another due to manyreasons.

1

Chao-Lin Liu, Tun-Wen Pai, Chun-Tien Chang, and Chang-Ming Hsieh (2001) Path-planningalgorithms for public transportation systems,, 1061-1066.
2
China Biographical Database: https://projects.iq.harvard.edu/cbdb
3Here, D stands for “daughter”, H for “husband”, and B for “brother”.
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Figure 2: A binary relationship matrix and its square)LJXUH00We can define operations for relationship inferences with a relationship matrix (RM). The
simplest choice is to convert cells that have known relationships into 1s and others into 0s. We
convert the matrix in Figure 1 into the M and compute its square in Figure 2. Note that the 1 in
M2 is informative. It indicates that P3 is P1ʼs fatherʼs daughterʼs husbandʼs brother.
0 that the powers of an 0
0 is a natural result of the definition of
The fact
RM are informative0
multiplicationof matrices.
Let M2x,y denote the cell (x,y) of0M2. We computeM2x,y based on thedefinition in (1). When we convert an RM into a binary RM, a cell is one only if the cell represents
an existing relationship. Hence, definition (1) essentially states that Px and Py has relationships if
there exists a person P z such that (1) P z is a relative of P x and (2) P y is a relative of P z.
0
Furthermore, the value of M2x,y is the total number of such two-step relationships.

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In addition to counting the indirect relationships when computing (1),0 we can concatenate the0
relationshipsof M and M to obtain a two-step relationship for Px and Py. For instance,
concatenating“F”, “2”, and “DHB” to obtain “1̲F̲2̲DHB̲3”,
in the computation for the M2 inB)BB'+%B
0
Figure 2, will record one indirect
P 1 and P 3 and the intermediate
0 relationship0 between
0participants. This operation of concatenation and recording can be achieved by software easily.

and Extensions
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0

We can easilyB)BB'+%B
generalize the implication of (1) to consider
0 the semantics of the values in the$SSOLFDWLRQVDQG([WHQVLRQV
n-th power of a binary RM, M, to achieve the following theorem.
Theorem 1. Let M denote a binary relationship matrix as we explained above. A person Px and aperson Py are relatives if there is a ρ≥ such that Mρx,y>0.
0 are 360,000 individuals in the 2015 version of CBDB, and we should be able to identify
There
clusters0of individuals that represent families. Although Theorem 1 is applicable for this task, a
7KHRUHP
more efficient method is to apply the ideas of identifying connected components in$SSOLFDWLRQVDQG([WHQVLRQV
mathematical graphs. The basic0
idea is quite simple: individuals that have relationships belong toa family and members of different families cannot have any relationships.
Corollary0 1 allows us to find the shortest relationships between two individuals. If the kin
relationships in the original RM are genuinely basic, e.g., father, month, son, daughter, husband,
7KHRUHP0 and sister, then we could find the most direct relationships between individuals
wife, brother,with the corollary.
0
Corollary 1. Let M denote the binary RM of an RM. By finding the smallest σ such that Mσx,y ispositive, we identify the relationship between x and y that has the fewest number ofintermediate participants.
In practice, not all kin relationships recorded in historical documents are basic, and extended
relationships such as “DHB” may be recorded. As a consequence, a “shortest path” as defined in
Corollary 1 may not be the most direct path between two individuals.
We can define the “lengths” of relationships to quantify the distances between individuals.4 Take
4

Similar quantification methods were adopted by Professor Michael Fuller when he designed abackend service for CBDB. Similar quantification methods were also described in the following
working paper.
Ke Deng, Peter K. Bol, Stuart M. Shieber, and Jun S. Liu. Building a Kinship Network for Political
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JOHQ
JADH 2017

“DHB” as an example. That Py is a “DHB” of Px means that Py is one generation after Px, and we
can define the g-len of “DHB” as -15. Analogously,
VOHQ the g-len of “F” is 1. The g-len of a path is thesum of the g-lens of all relationships in the path, so the g-len of “F̲2̲DHB” is 0. The length of a
relationship can encode the “sideway” distances as well. We can define the s-lens of “DHB” and“F” to be 2 and 0, respectively. In practice, there are only few hundreds of relationships in CBDB,
so it is feasible to annotate the relationships with their lengths.

Building Family Networks%XLOGLQJ)DPLO\1HWZRUNVThe upper part of Figure 3 shows three actual paths6 between individuals in CBDB. We canconstruct a family network that is consistent with the paths, and provide the network to domain
FRQVLVWHQW
experts for verification.


1$0(B%B1$0(B6B1$0( LH1$0(bVEURWKHULV1$0(
1$0(bVVRQLV1$0( 1$0(B))B1$0(B66B1$0(B6B1$0(
1$0(B)B1$0(B6B1$0(B6B1$0(Figure 3: Creating a network with paths)LJXUH

Figures in History.
5
D is a relationship from an earlier generation to a later generation (-1). Both H and B are
relations for the same generations (+0). Hence the g-cost of “DHB” is -1+0+0=-1.
6
The actual names in Chinese are NAME1 for 呂弸中 (lu3 peng2 zhong1), NAME2 for 呂大器 (lu3da4 qi4), NAME3 for 呂祖謙 (lu3 zu3 qian1), NAME4 for 呂祖儉 (lu3 zu3 jian3), and NAME5 for 呂
喬年 (lu3 qiao2 nian3).
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Semi-automatic reconstruction of category for
integrated ordinance database
Takanori Kawashima (National Diet Library),
Takashi Harada (Doshisha University)
Introduction
Our project is building "Ordinance Web Archive Database"[1], which collect ordinance of 1727
Japanese local governments and provide full-text search for about one million ordinances. In thisdatabase, facet filtering with categories is not implemented although each ordinance hasheadings like "social welfare" or "agriculture and forestry". Category facet seems to be useful tonavigate through complicated law system especially for novice, but it is not provided for thefollowing reason. Because Japanese law does not strictly regulate legal system of local
government' ordinance, each municipality is creating their own headings/categories forordinances. So one municipality has category named "social welfare", and the other has "publicwelfare", but they contain many similar ordinances.
Actually, the title or content of each ordinance is similar in many municipalities. Kakuta
calculated editorial distance for each text of ordinances and found there exists about 500
patterns of ordinances in 802 municipalities [2].

MethodThere are about 700 variations in top level headings, and about 5,000 variations in second level
headings in character strings, so it is hard to create categories manually. Therefore, we've triedcreating it automatically.
In this case, creating categories anew is not necessary, because there are existing categories.
What we have to do is reconstructing them and which can be done by clustering them. The
outline of the clustering algorithm is as follows.

Each ordinance has top level heading (h1) and second level heading (h2). Conversely, each h1 or
h2 has several child ordinances. We use title words (noun) in these ordinances as feature,
because same kind of ordinance has similar title across municipality. To create ordinancefeature vector, CBoW model vector [3] trained with word2vect and Japanese Wikipedia [4] isutilized to absorb the blurring of terms used and reduce dimension of feature vector. The
feature vector is sum of CBoW model vectors from words included in child ordinances.

Some municipality create less-meaningful category, such as "ordinance collection". To exclude
them, all ordinances are redistributed to categories which are most close in Pearson Coefficientof feature vectors. With this, some categories will have no child ordinances and they are
excluded.

We chose k-means++ as clustering method using Pearson Coefficient as distance measure. The
numbers of clusters are decided as 10 for h1, and 30 for h2 for test data, as a result of elbow
method with try and error. The purpose of this algorithm is to create comprehensive categories,
so some arbitrariness is no problem in this case.

Average feature vectors are calculated for each category clusters. Then, the parent h1 category
clusters of h2 category clusters are decided which has minimum Pearson Coefficient of feature
vectors.

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The name of a category cluster is decided by name of child categories. Words appearing in
more than one-third of child categories are elected as name of categories.
To evaluate the algorithm, ordinances of one prefecture is selected as test data, which consistof 762 h1 headings, 3,058 h2 headings and total of 47,636 ordinances.

Results
An example of generated categories is as shown below.
h1h2

General Rule

City Regime, Commendation, The Part*

Fire fighting

Fire fighting

SalaryTravel expenses/Remuneration, Personnel affairs, Statusof civil servant/headcount, Group of civil servant, Serviceof civil servant

Finance

Society, Contract, Environment*, House*, Accounting,
Medicine for late elderly people*, Tax

Parliament

Election committee

Sentence #City Regime*

Education

Documents*, Equity Committee*, Agriculture Committee/
Board of Education, Society*, Public announcement
ceremony*, Affiliation*, Fixed asset appraisal review
committee/Audit committee*, Mayor*, Cultural property,
Public relations*Public enterprise/Construction

Sewage, Public announcement ceremony, City

Categories marked # are h1 category clusters which is not suitable for top level category. The
name "Sentence" is derived from "Above Sentence". These categories should be merged to"General Rule", as "City Regime" h2 categories appear twice. Other top level categories are
suitable, but "Education" has lot of inappropriate * marked h2 category clusters, which suggest
there is need of h1 categories like "committee" or "social welfare".
To evaluate accuracy of h2 category clusters, 489 (1%) randomly selected ordinances arechecked whether they are a) under the correct category, b) under irrelevant category, c) can't
be decided. The result is a) 48%, b) 43%, c) 7% (conducted by 3 experimenters and category
which get 2 or more votes are counted. When the vote broke, it is counted as "c".).
About 60% of ordinances decided as (b) or (c) is under "Finance-Society" or "Education-Mayor".
Former category contains many ordinances about children, handicapped, or elderly welfare,which includes financial aspect of policy (child medical expenses payment ordinance), andfacility (General welfare center ordinance) or abstract one (Home childcare ordinance). Judged
as (b) in experiments includes facility or abstract kind of welfare ordinances, so "Finance" h1 ismisleading in this case. Latter category includes variety of ordinances such as "Ordinance toestablish a special group for city promotion strategy". It seems "Mayor" category includes
ordinances which are directly under mayor's control, or something cannot be categorized toother categories, and this leads the inaccuracy.
Excluding (b) and (c) of these 2 categories, 69% of ordinances are under correct category.

Conclusions and Future Works
Although the accuracy of the result is not high, the method is almost automatic and easily
expandable to whole municipality. To improved accuracy, it is possible to redistribute ordinances
again to constructed category clusters. For further research, we will ask evaluation of generated
98

JADH 2017

categories by law professional, and it is also useful to modify generated categories byprofessional. Furthermore, by improving this algorithm, it is possible to apply this to integrated
database of other kind.
References[1] http://joreislis.doshisha.ac.jp/[2] Kakuta T. Report about the progress and future of eLen project, Journal of Law and Politics,
v.252, 2013, p.247-234[3] Mikolov T, Chen K, Corrado G, Dean J, Efficient Estimation of Word Representations in
Vector Space, arXiv:1301.3781, 2013[4] http://www.cl.ecei.tohoku.ac.jp/~m-suzuki/jawiki̲vector/

99

JADH 2017

Employing Syntactic Features in Authorship Attributionof Three Writers: Alice Bradley Sheldon, Ernest
Hemingway, and Theodore Sturgeon
Miki Kimura (Meiji University)
Abstract
This study performs a quantitative authorship attribution analysis on the works of Alice Bradley
Sheldon (1915-1987), an American writer of feminist science fiction who primarily used the pen
name James Tiptree, Jr., to disguise her true gender. Given that Sheldon masqueraded as themale James Tiptree, Jr., for almost a decade, many critics have discussed the authorʼs gender.
Silverberg (1975) insisted, with reference to the style of Ernest Hemingway, that James Tiptreeʼsstories were written by a man. In this study, we performed a quantitative stylistic analysis of
Sheldonʼs work, comparing it with that of Ernest Hemingway and Theodore Sturgeon, a malescience fiction writer whose career overlapped with Sheldonʼs. By comparing all of Alice
Sheldonʼs works with those of Sturgeon and Hemingway, we hoped to find clues about Sheldonʼsallegedly masculine writing style. The analysis used syntactic variables as discriminants,
specifically the distribution of parts of speech (POS). Four kinds of statistical analyses wereconducted by two unsupervised algorithms (a cluster analysis and a principal component
analysis) and two supervised learning algorithms (using support vector machines and random
forests). The study examined inter-author variations between the three designated authors. The
results show two kinds of supervised learning methods can detect inter-author variations.
Furthermore, based on the multi-dimensional scaling (MDS) plot of the random forests results,
Alice Sheldonʼs style is similar to that of Ernest Hemingway, just as many literary critics aver.

Introduction
This paper presents a case study of a quantitative authorship attribution dealing with the worksof Alice Bradley Sheldon (1915-1987), an American writer of feminist science fiction. When she
first began writing in 1967, Sheldon used the male pen name James Tiptree, Jr., both to conceal
her identity and as a commercial strategy. In this way, she successfully disguised her gender.
During the decade that the name James Tiptree, Jr., concealed Sheldonʼs identity, many critics
discussed the authorʼs gender. The best-known literary critique was Robert Silverbergʼs
introduction to the 1975 collection of Tiptreeʼs stories,
In the3,000-word essay, titled “Who Is Tiptree? What Is He?”, Silverberg (1975) wrote:
Hemingway was a deeper and trickier writer than he pretended to be; so too with Tiptree, whoconceals behind an aw-shucks artlessness an astonishing skill for shaping scenes andmisdirecting readers into unexpected abysses of experience. And there is, too, that prevailingmasculinity about both of them.
Later, after Alice Sheldonʼs true identity was revealed, Lefanu (1989) also compared Tiptreeʼsstories to Hemingwayʼs, noting that Tiptreeʼs manner of writing was masculine.
In order to investigate the similarities and dissimilarities between Alice Sheldonʼs works andthose of Ernest Hemingway. However, there were many confounding factors when comparing
their works, such as different genres and periods. To overcome these problems, a corpus of
Theodore Sturgeonʼs short stories was also compiled. Sturgeon (1918-1985) was an American
writer of short science fiction stories for over 30 years and a contemporary of Sheldonʼs.
Note that if one of the approaches, such as cluster analysis, is sensitive enough to make intraauthor discriminations between works attributed to Tiptree and those attributed to Raccoona
Sheldon, then the same approach will also detect inter-author variations between Alice Sheldon
and the other two authors. On the other hand, it is possible that the author discriminators
applied to these corpora differentiate between Alice Sheldon and the other two authors, but failto discriminate between Alice Sheldonʼs two pseudonyms. The latter result would mean that
Alice Sheldon failed to disguise her true identity by using the pen names Thus, by comparing
Alice Sheldonʼs works under her two pen names with the works of Hemingway and Sturgeon, we
100

JADH 2017

hoped to find clues about Sheldonʼs allegedly masculine writing style.

Data and Methods
The three corpora compiled for this study contained all of Alice Sheldonʼs published work underboth her pen names (72 works with 865,802 word tokens), all of Hemingwayʼs work (69 works
with 271,475 word tokens), and all of Sturgeonʼs work (222 works with 1,952,895 word tokens).
In order to maintain consistent sample sizes, 70 of Sturgeonʼs works were randomly selectedfrom his corpus.
The emphasis in this research is primarily on variations between the three authorsʼ works. In
addition, the study also compared the results of the quantitative stylometry with the opinions ofliterary critics such as Silverberg (1975), Lefanu (1989) and others. Following the approaches of
Hirst and Feiguina (2007), Hou and Jiang (2016), and Kimura (2017), our quantitative analysis
used syntactic variables as effective discriminants (specifically, the distribution of parts ofspeech: POS). We chose unigram of POS as variables in this analysis. Furthermore we carriedout two kinds of unsupervised statistical analyses, namely cluster analysis and principal
component analysis (PCA), and two kinds of supervised statistical methods, namely support
vector machines (SVM) and random forests. The results of these statistical analyses werecompared with each other.

Results
Figure 1 shows the result of cluster analysis with multi-scale bootstrap resampling. As revealedby the dendrogram for the cluster analysis, no James Tiptree cluster or Raccoona Sheldon
cluster was detected. Alice Sheldon tried to vary her writing style for her two pseudonyms, but
the syntactic features used in these analyses revealed no significant variation between thestories published under the two different pen names.
For the unsupervised methods, signs of inter-author variations were also considered, but novisible clusters or groups were seen for Alice Sheldon, Hemingway or Sturgeon. In conclusion,
the unsupervised method was unable to detect either intra-author or intra-author variations forthe three authors in the study.
Because of the difference of the sample size, when trying to inspect the intra-author variation in
Alice Sheldonʼs works the problem of overfitting occurs. Therefore, the intra-author variations
will not be inspected employing supervised learning methods. The classification accuracy of the
SVM analysis for discriminating between the three authors is 99.05%.

Figure 1: Cluster Dendrogram (Distance: Correlation, Cluster Method: Average) with Multi-Scale
Bootstrap Resampling101

JADH 2017

Table 1: Results from Support Vector Machines (SVM) with a Cross Validation
Alice Sheldon

Ernest Hemingway

Theodore Sturgeon

71

1

0

Ernest Hemingway

0

68

1

Theodore Sturgeon

0

0

70

Alice Sheldon

The second of the two supervised learning methods used random forests. Its results had aclassification accuracy of 94.31%. From these results, we conclude that the supervised learning
methods can successfully discriminate between the styles of Alice Sheldon, Hemingway, andSturgeon.
Table 2: Results from Random Forests
Alice Sheldon
Alice Sheldon

Ernest Hemingway Theodore Sturgeon

Class Error

67

5

0

0.06944444

Ernest Hemingway

5

62

2

0.10144928

Theodore Sturgeon

0

0

70

0.00000000

By employing random forests, we can obtain proximity of data. Figure 2 gives a MDS plot of therandom forests results based on proximity. The circles represent works by Alice Sheldon, thetriangles represent those by Hemingway, and the crosses represent those by Theodore
Sturgeon. Works that were misclassified are represented in their IDs. The plot shows evidenceof three definite clusters for Alice Sheldon, Hemingway, and Sturgeon. Thus, by employing an
MDS plot, inter-author variations can be detected. However, according to this plot, the cluster
for Sturgeon is a little farther from the other two clusters. In this sense, Alice Sheldonʼs style isrelatively similar to that of Ernest Hemingway, just as many literary critics have said. Moreover,based on Gini index, variables that are effective for discrimination are identified. For example,
the possessive ending, singular nouns, and plural nouns are effective for this kind of
discrimination.

Figure 2: MDS Plot

102

JADH 2017Conclusion
This analysis makes two points. The first point addresses Le Guinʼs suggestion that the works
by James Tiptree, Jr. and those by Raccoona Sheldon are different in terms of writing style. In
fact, Alice Sheldon tried to change the content of her writing under her female pen name.
However, as noted earlier, the statistical analyses employed for this study could not detect intraauthor variations in Alice Sheldonʼs texts. In other words, Alice Sheldon failed to change her
writing style and did not produce two different bodies of work to match her two differently
gendered pseudonyms. The second point of this analysis considers the two kinds of supervised
statistical analyses which did detect inter-author variations between the three authorsʼ writing
styles. Many literary critics suggest that James Tiptreeʼs manner of writing is similar toHemmingway's, in that both have a masculine writing style. However, our analysis did not provethat masculinity is the stylistic trait shared by Hemingway and Tiptree, though it does prove that
their styles were similar. However, according to MDS plot, the cluster for Sturgeon is a little
farther from the other two clusters. In this sense, Sheldonʼs style is relatively similar to that ofHemingway, just as many literary critics have said.
References[1] Hirst, G., & Feiguina, O. G. (2007). Bigrams of syntactic labels for authorship discriminationof short texts.
, 22(4), 405-417.
[2] Hou, R., & Jiang, M. (2014). Analysis on Chinese quantitative stylistic features based on text
mining.
, 31(2), 357-367.
[3] Kimura, M. (2017). Quantitative Authorship Attribution of Two Authors and Evaluation of
Variables − Alice Bradley Sheldon and Ernest Hemingway−, IPSJ SIG Technical Report,
2017-CH-113, 1-6.
[4] Larbalestier, J. (2002).
. Connecticut; Wesleyan
University Press.
[5] Lefanu, S. (1988).
. Bloomington and Indianapolis; Indiana
University Press.
[6] Silverberg, R. (1975). Who Is Tiptree, What Is He?,. ⅳ-ⅹⅷ.

103

JADH 2017

Annotation of ʻWord List by Semantic Principlesʼ Labelsfrom the ʻCorpus of Historical Japaneseʼ
Heian Period Series− Trial Annotation on Tosa Nikki and Taketori Monogatari −Masayuki Asahara (National Institute for Japanese Language andLinguistics), Nao Ikegami (Saitama University), Yutaka Hara (none),
Sachi Kato (National Institute for Japanese Language and Linguistics),
Tai Suzuki (none)ʻWord List by Semantic Principlesʼ (Bunrui Goi Hyo; hereafter WLSP) is a Japanese thesaurus
which includes around 100,000 lexical items. The thesaurus offers two kinds of information: one
regarding the syntactic feature (Rui), the other regarding the three-layered semantic features(Bumon, Chu-Koumoku, and Bunrui-Komoku). ̀Nihon Koten Taishou Bunrui Goi Hyoʼ (hereafter,
historical-WLSP) was published in 2014, which is an enhanced version of WLSP, to coverhistorical Japanese. The semantic feature labels are based on the contemporary WLSP and
extended to the senses that appeared only in historical Japanese. The book also includes a word
list with the word frequency of 17 works from ̀Manyo-shuʼ in the Nara period to ̀Tsuretsuregusaʼ in the Kamakura period. However, the word senses in the book are not disambiguated by
the contextual information.
In order to resolve the word sense ambiguity issues, we performed a trial annotation of word
sense disambiguation. We selected two samples -- ̀Tosa Nikki (Tosa Diary)ʼ and ̀Taketori
Monogatari (The Tale of the Bamboo Cutter)ʼ -- from the ̀Corpus of Historical Japaneseʼ of the
Heian Period Series.
The annotation comprised the following three steps: (a) extract the contemporary WLSP labels,(b) extract the historical WLSP labels, and (c) choose the appropriate labels or (d) newly
introduce WLSP labels.
In step (a), all possible contemporary WLSP labels are extracted for each morpheme. In step (b),the extended word senses are appended for each morpheme. In step (c), a human annotatorchooses one sense from the all the possible word senses, and in step (d), introduces a new word
sense when none of the word sense candidates is appropriate in the context.
We report the statistics of ̀Taketori Monogatariʼ and ̀Tosa Nikkiʼ, which have 12,757 and 8,208
morphemes, respectively. Annotations of WLSP labels cover 41.9% (5,348/12,757) and 42.4%(3,481/8,208) of the morpheme tokens.
From the 8829 (Taketori 5348, Tosa 3481) tokens, 1703 (Taketori 1060, Tosa 643) annotatedtokens are from historical WLSP labels. Therefore, 19.3% of the annotated tokens are word
senses only in historical Japanese.

Table 1: The statistics of the syntactic feature (Rui)
1
Noun

2
Verb

3
Adj, Adv

Taketori

2318
43.3%

2252
42.1%

706
13.2%

72
1.3%

5348

Tosa

1710
49.1%

1272
36.5%

454
13.0%

45
1.3%

3481

Total

4028
45.6%

3524
39.9%

1160
13.1%

117
1.3%

8829

104

4 TotalOthersJADH 2017

Table 1 shows the syntactic feature level of the two samples.
The MVR (Modifier-Verb Rate) of Taketori and Tosa are 31.3% and 35.6%, respectively.
To make a more meaningful comparison, we introduced separation by sentence types, such asʻdescriptiveʼ 地の文 , ʻpoemʼ 歌 , ʻquoteʼ 会話 , and ʻletterʼ 手紙 , in Table 2. The descriptive part of
the Noun Rate and MVR are nearly the same as that in the preceding research by Fujiike (2013)
in which the rates are estimated by the POS information.
Table 2: The statistics of the syntactic feature (Rui) by sentence types
1体
Noun

2用
Verb

3相
Adj, Adv

4他
Other(subtotal)

Noun
Rate

MVR

:descriptive

1147

1230

289

28

2694

42.5%

23.5%

:poem

60

49

13

2

124

48.3%

26.5%

:quote

1026

891

375

39

2331

44.0%

42.0%

:letter

85

82

29

3

199

42.7%

35.4%

(subtotal)

2318

2252

706

72

5348

43.3%

31.3%

:descriptive

1298

966

368

38

2670

48.6%

38.1%

:poem

267

202

48

2

519

51.4%

23.8%

:quote

145

104

38

5

292

49.8%

37.7%

(subtotal)

1710

1272

454

45

3481

49.1%

35.7%

Total

4028

3524

1160

117

8829

Taketori

Tosa

Table 3 shows the statistics of the semantic features at the top level (Bumon). Interestingly, thesemantic feature label distribution of the two samples are similar, although the syntactic featurelabel distribution of the two samples shows different types of writing styles.
Table 3: The statistics of the semantic features at the top (Bumon)

Taketori
Tosa
Total

1 関係
Relation

2 主体
Subject

3 活動
Action4 生産物
Product

5 自然
Nature9 枕詞
Poetic

2335

577

1722

229

485

43.7%

10.8%

32.2%

4.3%

9.1%

0.0%

1540

360

1002

167

411

1

44.2%

10.3%

28.8%

4.8%

11.8%

0.0%

3875

937

2724

396

896

1

43.9%

10.6%

30.9%

4.5%

10.1%

0.0%

Subtotal
5348
3481
8829

Table 4 shows the top five frequent word senses in the most fine-grained semantic labels.
The distribution of the finest semantic features is slightly different between the two samples.

105

JADH 2017

Table 4: The top five finest semantic features (Bunruikomoku)
Rank

FeatureTotal*

The meaning of label

Taketori

Tosa

Total

*

5348

3481

8829

1

1010

Relation-Thing-Pronoun

288(5.3%)

163(4.6%)

451(5.1%)

2

1200

Relation-Existence-Existence

243(4.5%)

136(3.9%)

379(4.2%)

3

3100

Action-Language-Linguistic Activity

271(5.0%)

91(2.6%)

362(4.1%)

4

1527

Action-Operation-Reciprocating150(2.8%)

101(2.9%)

251(2.8%)

5

2000

Subject-Human-Human

127(2.3%)

98(2.8%)

225(2.5%)

In our future work, we plan to annotate WLSP labels on other data in Heian periods and examine
large-scale comparison among the samples.

Acknowledgements:
This work was supported by JSPS KAKENHI Grant Numbers 17H00917, 15K12888 and a
project of Center for Corpus Development, NINJAL.
References:[1] Yumi Fujiike, (2014) ʻ品詞比率から見る中古和文テキストの特徴ʼ (in Japanese),『日本語学会 2014年度春季大会予稿集』[2] Kokuritu-Kokugo-Kenkyusho, (2004) ʻ 分 類 語 彙 表 ʼ, Bunrui Goihyo (Word List by Semantic
Principles, Revised and Enlarged Edition) (in Japanese), Dainippontosho.
[3] Tatsuo Miyajima, Hisao Ishii, Seiya Abe, and Tai Suzuki, (2013) ʻ日本古典対照分類語彙表ʼ (inJapanese), Kasamashoin.

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Building Networks and Creating Access in
Early Modern Europe
Lisa Tagliaferri (City University of New York)The literary contributions of marginalized writers have too often been neglected due to theprivileging of an exclusive canon. As vernacular languages gained ground in an educated climate
previously controlled by Latin literacy, more readers gained greater access to information andcould become knowledge producers in their own right. One of the most prolific writers of 14thcentury vernacular Italian was Catherine of Siena, a writer of nearly 400 extant letters, a booklength dialogue, and copied prayers. Though Catherine did work to learn to read Latin, she
consciously and emphatically would choose Sienese Italian as her language of writing andspeech. As a spiritual figure doing good work in the world, offering comfort, advice, and care to
the prisoners, penitent women, and ill of Siena, she worked to include rather than exclude
others. Leveraging network analysis and visualization, and considering how reader communities
serve to provide access to information via the technology of writing, this paper seeks to show ahistorical example of the building of communities through literacy, authorship, and text.
This paper demonstrates the literary value of Catherineʼs writing through contextualizing her
work among the corpus of contemporary texts, and analyzing her reception in the Renaissanceperiod of Italy and England. This project situates Catherine within a larger textual communitythat adds to her authority as a writer with an influence beyond that of her language, culture, andcentury. Guided by humanistic inquiry concerning the interpretation of authorship andtranslation, and supported by quantitative digital methods, this work features an interdisciplinary
approach to recover a marginalized writer. For this project, I have analyzed Catherineʼs body oftexts against Danteʼs and Petrarchʼs major works, as well as the Middle English translation of her
dialogue. This was done by my development of a script in Perl which cleaned up text files that I
then analyzed through text mining in Python and Java-based topic modeling to see major topics
across corpora. A substantial portion of the digital humanities analysis has been a complete
network visualization of Catherineʼs correspondents across geographical Europe, and acrossclass lines and religious affiliations. This work was done in R, JavaScript, and Python using D3
packages which export the information to visualizations that are ready for web interactive use.
These interactive visualizations best express the reach of Catherineʼs textual production, andprovide a comparison point to my traditional archival approaches that concern the history of
medieval and early modern books. The website compendium to the dissertation, https://caterina.
io, will continue to be developed to store relevant digital and interactive files, and to have apublic-facing dimension to the research.

107Proceedings of JADH conference, vol. 2017
Published by the Faculty of Culture and Information Science, Doshisha University
1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto 610-0394 Japan
Online edition: ISSN 2432-3144 Print edition: ISSN 2432-3187
Editor: Akihiro Kawase
Copyright 2017 Japanese Association for Digital Humanities