pwc-archive/papers-with-abstracts huggingface dataset:
Original-Transcribed Text Alignment for Manyosyu Written by Old Japanese Language
Authors: Teruaki Oka, Tomoaki Kono
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Machine Translation, Translation

We are constructing an annotated diachronic corpora of the Japanese language. In part of thiswork, we construct a corpus of Manyosyu, which is an old Japanese poetry anthology. In thispaper, we describe how to align the transcribed text and its original text semiautomatically to beable to cross-reference them in our Manyosyu corpus. Although we align the original charactersto the transcribed words manually, we preliminarily align the transcribed and original charactersby using an unsupervised automatic alignment technique of statistical machine translation toalleviate the work. We found that automatic alignment achieves an F1-measure of 0.83; thus, each poem has 1{--}2 alignment errors. However, finding these errors and modifying them are less workintensiveand more efficient than fully manual annotation. The alignment probabilities can beutilized in this modification. Moreover, we found that we can locate the uncertain transcriptionsin our corpus and compare them to other transcriptions, by using the alignment probabilities.
Feelings from the Past---Adapting Affective Lexicons for Historical Emotion Analysis
Authors: Sven Buechel, Johannes Hellrich, Udo Hahn
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Emotion Recognition, Word Embeddings

We here describe a novel methodology for measuring affective language in historical text by expanding an affective lexicon and jointly adapting it to prior language stages. We automatically construct a lexicon for word-emotion association of 18th and 19th century German which is then validated against expert ratings. Subsequently, this resource is used to identify distinct emotional patterns and trace long-term emotional trends in different genres of writing spanning several centuries.
Automatic parsing as an efficient pre-annotation tool for historical texts
Authors: Hanne Martine Eckhoff, Aleks Berdi{\v{c}}evskis, rs
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Sentence

Historical treebanks tend to be manually annotated, which is not surprising, since state-of-the-art parsers are not accurate enough to ensure high-quality annotation for historical texts. We test whether automatic parsing can be an efficient pre-annotation tool for Old East Slavic texts. We use the TOROT treebank from the PROIEL treebank family. We convert the PROIEL format to the CONLL format and use MaltParser to create syntactic pre-annotation. Using the most conservative evaluation method, which takes into account PROIEL-specific features, MaltParser by itself yields 0.845 unlabelled attachment score, 0.779 labelled attachment score and 0.741 secondary dependency accuracy (note, though, that the test set comes from a relatively simple genre and contains rather short sentences). Experiments with human annotators show that preparsing, if limited to sentences where no changes to word or sentence boundaries are required, increases their annotation rate. For experienced annotators, the speed gain varies from 5.80{\%} to 16.57{\%}, for inexperienced annotators from 14.61{\%} to 32.17{\%} (using conservative estimates). There are no strong reliable differences in the annotation accuracy, which means that there is no reason to suspect that using preparsing might lower the final annotation quality.
A Web-based Tool for the Integrated Annotation of Semantic and Syntactic Structures
Authors: Richard Eckart de Castilho, {\'E}va M{\'u}jdricza-Maydt, Seid Muhie Yimam, Silvana Hartmann, Iryna Gurevych, Anette Frank, Chris Biemann
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Relation Extraction, Slot Filling

We introduce the third major release of WebAnno, a generic web-based annotation tool for distributed teams. New features in this release focus on semantic annotation tasks (e.g. semantic role labelling or event annotation) and allow the tight integration of semantic annotations with syntactic annotations. In particular, we introduce the concept of slot features, a novel constraint mechanism that allows modelling the interaction between semantic and syntactic annotations, as well as a new annotation user interface. The new features were developed and used in an annotation project for semantic roles on German texts. The paper briefly introduces this project and reports on experiences performing annotations with the new tool. On a comparative evaluation, our tool reaches significant speedups over WebAnno 2 for a semantic annotation task.
Challenges and Solutions for Latin Named Entity Recognition
Authors: Alex Erdmann, er, Christopher Brown, Brian Joseph, Mark Janse, Petra Ajaka, Micha Elsner, Marie-Catherine de Marneffe
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Active Learning, Domain Adaptation, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Translation

Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90{\%} F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality.
Geographical Visualization of Search Results in Historical Corpora
Authors: Florian Petran
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We present ANNISVis, a webapp for comparative visualization of geographical distribution of linguistic data, as well as a sample deployment for a corpus of Middle High German texts. Unlike existing geographical visualization solutions, which work with pre-existing data sets, or are bound to specific corpora, ANNISVis allows the user to formulate multiple ad-hoc queries and visualizes them on a map, and it can be configured for any corpus that can be imported into ANNIS. This enables explorative queries of the quantitative aspects of a corpus with geographical features. The tool will be made available to download in open source.
Implementation of a Workflow Management System for Non-Expert Users
Authors: Bart Jongejan
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Management, Optical Character Recognition (OCR)

In the Danish CLARIN-DK infrastructure, chaining language technology (LT) tools into a workflow is easy even for a non-expert user, because she only needs to specify the input and the desired output of the workflow. With this information and the registered input and output profiles of the available tools, the CLARIN-DK workflow management system (WMS) computes combinations of tools that will give the desired result. This advanced functionality was originally not envisaged, but came within reach by writing the WMS partly in Java and partly in a programming language for symbolic computation, Bracmat. Handling LT tool profiles, including the computation of workflows, is easier with Bracmat{'}s language constructs for tree pattern matching and tree construction than with the language constructs offered by mainstream programming languages.
Integrating Optical Character Recognition and Machine Translation of Historical Documents
Authors: Haithem Afli, Andy Way
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Language Modeling, Language Modelling, Machine Translation, Optical Character Recognition, Optical Character Recognition (OCR), Translation

Machine Translation (MT) plays a critical role in expanding capacity in the translation industry. However, many valuable documents, including digital documents, are encoded in non-accessible formats for machine processing (e.g., Historical or Legal documents). Such documents must be passed through a process of Optical Character Recognition (OCR) to render the text suitable for MT. No matter how good the OCR is, this process introduces recognition errors, which often renders MT ineffective. In this paper, we propose a new OCR to MT framework based on adding a new OCR error correction module to enhance the overall quality of translation. Experimentation shows that our new system correction based on the combination of Language Modeling and Translation methods outperforms the baseline system by nearly 30{\%} relative improvement.
Language technology tools and resources for the analysis of multimodal communication
Authors: L{\'a}szl{\'o} Hunyadi, Tam{\'a}s V{\'a}radi, Istv{\'a}n Szekr{\'e}nyes
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

In this paper we describe how the complexity of human communication can be analysed with the help of language technology. We present the HuComTech corpus, a multimodal corpus containing 50 hours of videotaped interviews containing a rich annotation of about 2 million items annotated on 33 levels. The corpus serves as a general resource for a wide range of re-search addressing natural conversation between humans in their full complexity. It can benefit particularly digital humanities researchers working in the field of pragmatics, conversational analysis and discourse analysis. We will present a number of tools and automated methods that can help such enquiries. In particular, we will highlight the tool Theme, which is designed to uncover hidden temporal patterns (called T-patterns) in human interaction, and will show how it can applied to the study of multimodal communication.
Large-scale Analysis of Spoken Free-verse Poetry
Authors: Timo Baumann, Burkhard Meyer-Sickendiek
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Speech Synthesis

Most modern and post-modern poems have developed a post-metrical idea of lyrical prosody that employs rhythmical features of everyday language and prose instead of a strict adherence to rhyme and metrical schemes. This development is subsumed under the term free verse prosody. We present our methodology for the large-scale analysis of modern and post-modern poetry in both their written form and as spoken aloud by the author. We employ language processing tools to align text and speech, to generate a null-model of how the poem would be spoken by a na{\"\i}ve reader, and to extract contrastive prosodic features used by the poet. On these, we intend to build our model of free verse prosody, which will help to understand, differentiate and relate the different styles of free verse poetry. We plan to use our processing scheme on large amounts of data to iteratively build models of styles, to validate and guide manual style annotation, to identify further rhythmical categories, and ultimately to broaden our understanding of free verse poetry. In this paper, we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features. We find that our methodology helps to extract differentiating features in the authors{'} speech that can be explained by philological insight. Thus, our automatic method helps to guide the literary analysis and this in turn helps to improve our computational models.
PAT workbench: Annotation and Evaluation of Text and Pictures in Multimodal Instructions
Authors: Ielka van der Sluis, Lennart Kloppenburg, Gisela Redeker
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

This paper presents a tool to investigate the design of multimodal instructions (MIs), i.e., instructions that contain both text and pictures. The benefit of including pictures in information presentation has been established, but the characteristics of those pictures and of their textual counterparts and the rela-tion(s) between them have not been researched in a systematic manner. We present the PAT Work-bench, a tool to store, annotate and retrieve MIs based on a validated coding scheme with currently 42 categories that describe instructions in terms of textual features, pictorial elements, and relations be-tween text and pictures. We describe how the PAT Workbench facilitates collaborative annotation and inter-annotator agreement calculation. Future work on the tool includes expanding its functionality and usability by (i) making the MI annotation scheme dynamic for adding relevant features based on empirical evaluations of the MIs, (ii) implementing algorithms for automatic tagging of MI features, and (iii) implementing automatic MI evaluation algorithms based on results obtained via e.g. crowdsourced assessments of MIs.
Semantic Indexing of Multilingual Corpora and its Application on the History Domain
Authors: Aless Raganato, ro, Jose Camacho-Collados, Antonio Raganato, Yunseo Joung
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Retrieval, Text Retrieval, Translation

The increasing amount of multilingual text collections available in different domains makes its automatic processing essential for the development of a given field. However, standard processing techniques based on statistical clues and keyword searches have clear limitations. Instead, we propose a knowledge-based processing pipeline which overcomes most of the limitations of these techniques. This, in turn, enables direct comparison across texts in different languages without the need of translation. In this paper we show the potential of this approach for semantically indexing multilingual text collections in the history domain. In our experiments we used a version of the Bible translated in four different languages, evaluating the precision of our semantic indexing pipeline and showing its reliability on the cross-lingual text retrieval task.
Tagging Ingush - Language Technology For Low-Resource Languages Using Resources From Linguistic Field Work
Authors: J{\"o}rg Tiedemann, Johanna Nichols, Ronald Sprouse
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Cross-Lingual Transfer

This paper presents on-going work on creating NLP tools for under-resourced languages from very sparse training data coming from linguistic field work. In this work, we focus on Ingush, a Nakh-Daghestanian language spoken by about 300,000 people in the Russian republics Ingushetia and Chechnya. We present work on morphosyntactic taggers trained on transcribed and linguistically analyzed recordings and dependency parsers using English glosses to project annotation for creating synthetic treebanks. Our preliminary results are promising, supporting the goal of bootstrapping efficient NLP tools with limited or no task-specific annotated data resources available.
The MultiTal NLP tool infrastructure
Authors: Driss Sadoun, Satenik Mkhitaryan, Damien Nouvel, Mathieu Valette
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Multilingual NLP

This paper gives an overview of the MultiTal project, which aims to create a research infrastructure that ensures long-term distribution of NLP tools descriptions. The goal is to make NLP tools more accessible and usable to end-users of different disciplines. The infrastructure is built on a meta-data scheme modelling and standardising multilingual NLP tools documentation. The model is conceptualised using an OWL ontology. The formal representation of the ontology allows us to automatically generate organised and structured documentation in different languages for each represented tool.
Tools and Instruments for Building and Querying Diachronic Computational Lexica
Authors: Fahad Khan, Bell, Andrea i, Monica Monachini
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

This article describes work on enabling the addition of temporal information to senses of words in linguistic linked open data lexica based on the lemonDia model. Our contribution in this article is twofold. On the one hand, we demonstrate how lemonDia enables the querying of diachronic lexical datasets using OWL-oriented Semantic Web based technologies. On the other hand, we present a preliminary version of an interactive interface intended to help users in creating lexical datasets that model meaning change over time.
Using TEI for textbook research
Authors: Lena-Luise Stahn, Steffen Hennicke, Ernesto William De Luca
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: Information Retrieval

The following paper describes the first steps in the development of an ontology for the textbook research discipline. The aim of the project WorldViews is to establish a digital edition focussing on views of the world depicted in textbooks. For this purpose an initial TEI profile has been formalised and tested as a use case to enable the semantical encoding of the resource {`}textbook{'}. This profile shall provide a basic data model describing major facets of the textbook{'}s structure relevant to historians.
Web services and data mining: combining linguistic tools for Polish with an analytical platform
Authors: Maciej Ogrodniczuk
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

In this paper we present a new combination of existing language tools for Polish with a popular data mining platform intended to help researchers from digital humanities perform computational analyses without any programming. The toolset includes RapidMiner Studio, a software solution offering graphical setup of integrated analytical processes and Multiservice, a Web service offering access to several state-of-the-art linguistic tools for Polish. The setting is verified in a simple task of counting frequencies of unknown words in a small corpus.
Could Machine Learning Shed Light on Natural Language Complexity?
Authors: Maria Dolores Jim{\'e}nez-L{\'o}pez, Leonor Becerra-Bonache
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: BIG-bench Machine Learning, Language Acquisition

In this paper, we propose to use a subfield of machine learning {--}grammatical inference{--} to measure linguistic complexity from a developmental point of view. We focus on relative complexity by considering a child learner in the process of first language acquisition. The relevance of grammatical inference models for measuring linguistic complexity from a developmental point of view is based on the fact that algorithms proposed in this area can be considered computational models for studying first language acquisition. Even though it will be possible to use different techniques from the field of machine learning as computational models for dealing with linguistic complexity -since in any model we have algorithms that can learn from data-, we claim that grammatical inference models offer some advantages over other tools.
Towards a Distributional Model of Semantic Complexity
Authors: Emmanuele Chersoni, Philippe Blache, Aless Lenci, ro
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: model, Sentence

In this paper, we introduce for the first time a Distributional Model for computing semantic complexity, inspired by the general principles of the Memory, Unification and Control framework(Hagoort, 2013; Hagoort, 2016). We argue that sentence comprehension is an incremental process driven by the goal of constructing a coherent representation of the event represented by the sentence. The composition cost of a sentence depends on the semantic coherence of the event being constructed and on the activation degree of the linguistic constructions. We also report the results of a first evaluation of the model on the Bicknell dataset (Bicknell et al., 2010).
CoCoGen - Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique
Authors: Str{\"o}bel Marcus, Elma Kerz, Daniel Wiechmann, Stella Neumann
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

We present a novel approach to the automatic assessment of text complexity based on a sliding-window technique that tracks the distribution of complexity within a text. Such distribution is captured by what we term {``}complexity contours{''} derived from a series of measurements for a given linguistic complexity measure. This approach is implemented in an automatic computational tool, CoCoGen {--} Complexity Contour Generator, which in its current version supports 32 indices of linguistic complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
Addressing surprisal deficiencies in reading time models
Authors: Marten van Schijndel, William Schuler
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

This study demonstrates a weakness in how n-gram and PCFG surprisal are used to predict reading times in eye-tracking data. In particular, the information conveyed by words skipped during saccades is not usually included in the surprisal measures. This study shows that correcting the surprisal calculation improves n-gram surprisal and that upcoming n-grams affect reading times, replicating previous findings of how lexical frequencies affect reading times. In contrast, the predictivity of PCFG surprisal does not benefit from the surprisal correction despite the fact that lexical sequences skipped by saccades are processed by readers, as demonstrated by the corrected n-gram measure. These results raise questions about the formulation of information-theoretic measures of syntactic processing such as PCFG surprisal and entropy reduction when applied to reading times.
Towards grounding computational linguistic approaches to readability: Modeling reader-text interaction for easy and difficult texts
Authors: Sowmya Vajjala, Detmar Meurers, Alex Eitel, er, Katharina Scheiter
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12

Computational approaches to readability assessment are generally built and evaluated using gold standard corpora labeled by publishers or teachers rather than being grounded in observations about human performance. Considering that both the reading process and the outcome can be observed, there is an empirical wealth that could be used to ground computational analysis of text readability. This will also support explicit readability models connecting text complexity and the reader{'}s language proficiency to the reading process and outcomes. This paper takes a step in this direction by reporting on an experiment to study how the relation between text complexity and reader{'}s language proficiency affects the reading process and performance outcomes of readers after reading We modeled the reading process using three eye tracking variables: fixation count, average fixation count, and second pass reading duration. Our models for these variables explained 78.9{\%}, 74{\%} and 67.4{\%} variance, respectively. Performance outcome was modeled through recall and comprehension questions, and these models explained 58.9{\%} and 27.6{\%} of the variance, respectively. While the online models give us a better understanding of the cognitive correlates of reading with text complexity and language proficiency, modeling of the offline measures can be particularly relevant for incorporating user aspects into readability models.