pwc-archive/papers-with-abstracts huggingface dataset:
Legacy language atlas data mining: mapping Kru languages
Authors: Dafydd Gibbon
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

An online tool based on dialectometric methods, DistGraph, is applied to a group of Kru languages of C{\^o}te d{'}Ivoire, Liberia and Burkina Faso. The inputs to this resource consist of tables of languages x linguistic features (e.g. phonological, lexical or grammatical), and statistical and graphical outputs are generated which show similarities and differences between the languages in terms of the features as virtual distances. In the present contribution, attention is focussed on the consonant systems of the languages, a traditional starting point for language comparison. The data are harvested from a legacy language data resource based on fieldwork in the 1970s and 1980s, a language atlas of the Kru languages. The method on which the online tool is based extends beyond documentation of individual languages to the documentation of language groups, and supports difference-based prioritisation in education programmes, decisions on language policy and documentation and conservation funding, as well as research on language typology and heritage documentation of history and migration.
Data Formats and Management Strategies from the Perspective of Language Resource Producers ― Personal Diachronic and Social Synchronic Data Sharing ―
Authors: Kazushi Ohya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management

This is a report of findings from on-going language documentation research based on three consecutive projects from 2008 to 2016. In the light of this research, we propose that (1) we should stand on the side of language resource producers to enhance the research of language processing. We support personal data management in addition to social data sharing. (2) This support leads to adopting simple data formats instead of the multi-link-path data models proposed as international standards up to the present. (3) We should set up a framework for total language resource study that includes not only pivotal data formats such as standard formats, but also the surroundings of data formation to capture a wider range of language activities, e.g. annotation, hesitant language formation, and reference-referent relations. A study of this framework is expected to be a foundation of rebuilding man-machine interface studies in which we seek to observe generative processes of informational symbols in order to establish a high affinity interface in regard to documentation.
Curation of Dutch Regional Dictionaries
Authors: Henk van den Heuvel, S, Eric ers, Nicoline van der Sijs
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper describes the process of semi-automatically converting dictionaries from paper to structured text (database) and the integration of these into the CLARIN infrastructure in order to make the dictionaries accessible and retrievable for the research community. The case study at hand is that of the curation of 42 fascicles of the Dictionaries of the Brabantic and Limburgian dialects, and 6 fascicles of the Dictionary of dialects in Gelderland.
Fostering digital representation of EU regional and minority languages: the Digital Language Diversity Project
Authors: Claudia Soria, Irene Russo, Valeria Quochi, Davyth Hicks, Antton Gurrutxaga, Anneli Sarhimaa, Matti Tuomisto
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Diversity

Poor digital representation of minority languages further prevents their usability on digital media and devices. The Digital Language Diversity Project, a three-year project funded under the Erasmus+ programme, aims at addressing the problem of low digital representation of EU regional and minority languages by giving their speakers the intellectual an practical skills to create, share, and reuse online digital content. Availability of digital content and technical support to use it are essential prerequisites for the development of language-based digital applications, which in turn can boost digital usage of these languages. In this paper we introduce the project, its aims, objectives and current activities for sustaining digital usability of minority languages through adult education.
Cysill Ar-lein: A Corpus of Written Contemporary Welsh Compiled from an On-line Spelling and Grammar Checker
Authors: Delyth Prys, Gruffudd Prys, Dewi Bryn Jones
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Language Modelling

This paper describes the use of a free, on-line language spelling and grammar checking aid as a vehicle for the collection of a significant (31 million words and rising) corpus of text for academic research in the context of less resourced languages where such data in sufficient quantities are often unavailable. It describes two versions of the corpus: the texts as submitted, prior to the correction process, and the texts following the user{'}s incorporation of any suggested changes. An overview of the corpus{'} contents is given and an analysis of use including usage statistics is also provided. Issues surrounding privacy and the anonymization of data are explored as is the data{'}s potential use for linguistic analysis, lexical research and language modelling. The method used for gathering this corpus is believed to be unique, and is a valuable addition to corpus studies in a minority language.
ALT Explored: Integrating an Online Dialectometric Tool and an Online Dialect Atlas
Authors: Martijn Wieling, Eva Sassolini, Sebastiana Cucurullo, Simonetta Montemagni
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper, we illustrate the integration of an online dialectometric tool, Gabmap, together with an online dialect atlas, the Atlante Lessicale Toscano (ALT-Web). By using a newly created url-based interface to Gabmap, ALT-Web is able to take advantage of the sophisticated dialect visualization and exploration options incorporated in Gabmap. For example, distribution maps showing the distribution in the Tuscan dialect area of a specific dialectal form (selected via the ALT-Web website) are easily obtainable. Furthermore, the complete ALT-Web dataset as well as subsets of the data (selected via the ALT-Web website) can be automatically uploaded and explored in Gabmap. By combining these two online applications, macro- and micro-analyses of dialectal data (respectively offered by Gabmap and ALT-Web) are effectively and dynamically combined.
LORELEI Language Packs: Data, Tools, and Resources for Technology Development in Low Resource Languages
Authors: Stephanie Strassel, Jennifer Tracey
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper, we describe the textual linguistic resources in nearly 3 dozen languages being produced by Linguistic Data Consortium for DARPA{'}s LORELEI (Low Resource Languages for Emergent Incidents) Program. The goal of LORELEI is to improve the performance of human language technologies for low-resource languages and enable rapid re-training of such technologies for new languages, with a focus on the use case of deployment of resources in sudden emergencies such as natural disasters. Representative languages have been selected to provide broad typological coverage for training, and surprise incident languages for testing will be selected over the course of the program. Our approach treats the full set of language packs as a coherent whole, maintaining LORELEI-wide specifications, tagsets, and guidelines, while allowing for adaptation to the specific needs created by each language. Each representative language corpus, therefore, both stands on its own as a resource for the specific language and forms part of a large multilingual resource for broader cross-language technology development.
A Computational Perspective on the Romanian Dialects
Authors: Alina Maria Ciobanu, Liviu P. Dinu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Dialect Identification, General Classification

In this paper we conduct an initial study on the dialects of Romanian. We analyze the differences between Romanian and its dialects using the Swadesh list. We analyze the predictive power of the orthographic and phonetic features of the words, building a classification problem for dialect identification.
The Alaskan Athabascan Grammar Database
Authors: Sebastian Nordhoff, Siri Tuttle, Olga Lovick
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence

This paper describes a repository of example sentences in three endangered Athabascan languages: Koyukon, Upper Tanana, Lower Tanana. The repository allows researchers or language teachers to browse the example sentence corpus to either investigate the languages or to prepare teaching materials. The originally heterogeneous text collection was imported into a SOLR store via the POIO bridge. This paper describes the requirements, implementation, advantages and drawbacks of this approach and discusses the potential to apply it for other languages of the Athabascan family or beyond.
Constraint-Based Bilingual Lexicon Induction for Closely Related Languages
Authors: Arbi Haza Nasution, Yohei Murakami, Toru Ishida
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Bilingual Lexicon Induction, Translation

The lack or absence of parallel and comparable corpora makes bilingual lexicon extraction becomes a difficult task for low-resource languages. Pivot language and cognate recognition approach have been proven useful to induce bilingual lexicons for such languages. We analyze the features of closely related languages and define a semantic constraint assumption. Based on the assumption, we propose a constraint-based bilingual lexicon induction for closely related languages by extending constraints and translation pair candidates from recent pivot language approach. We further define three constraint sets based on language characteristics. In this paper, two controlled experiments are conducted. The former involves four closely related language pairs with different language pair similarities, and the latter focuses on sense connectivity between non-pivot words and pivot words. We evaluate our result with F-measure. The result indicates that our method works better on voluminous input dictionaries and high similarity languages. Finally, we introduce a strategy to use proper constraint sets for different goals and language characteristics.
WTF-LOD - A New Resource for Large-Scale NER Evaluation
Authors: Lubomir Otrusina, Pavel Smrz
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper introduces the Web TextFull linkage to Linked Open Data (WTF-LOD) dataset intended for large-scale evaluation of named entity recognition (NER) systems. First, we present the process of collecting data from the largest publically-available textual corpora, including Wikipedia dumps, monthly runs of the CommonCrawl, and ClueWeb09/12. We discuss similarities and differences of related initiatives such as WikiLinks and WikiReverse. Our work primarily focuses on links from {``}textfull{''} documents (links surrounded by a text that provides a useful context for entity linking), de-duplication of the data and advanced cleaning procedures. Presented statistics demonstrate that the collected data forms one of the largest available resource of its kind. They also prove suitability of the result for complex NER evaluation campaigns, including an analysis of the most ambiguous name mentions appearing in the data.
Using a Language Technology Infrastructure for German in order to Anonymize German Sign Language Corpus Data
Authors: Julian Bleicken, Thomas Hanke, Uta Salden, Sven Wagner
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

For publishing sign language corpus data on the web, anonymization is crucial even if it is impossible to hide the visual appearance of the signers: In a small community, even vague references to third persons may be enough to identify those persons. In the case of the DGS Korpus (German Sign Language corpus) project, we want to publish data as a contribution to the cultural heritage of the sign language community while annotation of the data is still ongoing. This poses the question how well anonymization can be achieved given that no full linguistic analysis of the data is available. Basically, we combine analysis of all data that we have, including named entity recognition on translations into German. For this, we use the WebLicht language technology infrastructure. We report on the reliability of these methods in this special context and also illustrate how the anonymization of the video data is technically achieved in order to minimally disturb the viewer.
Crowdsourced Corpus with Entity Salience Annotations
Authors: Milan Dojchinovski, Dinesh Reddy, Tom{\'a}{\v{s}} Kliegr, Tom{\'a}{\v{s}} Vitvar, Harald Sack
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper, we present a crowdsourced dataset which adds entity salience (importance) annotations to the Reuters-128 dataset, which is subset of Reuters-21578. The dataset is distributed under a free license and publish in the NLP Interchange Format, which fosters interoperability and re-use. We show the potential of the dataset on the task of learning an entity salience classifier and report on the results from several experiments.
ELMD: An Automatically Generated Entity Linking Gold Standard Dataset in the Music Domain
Authors: Sergio Oramas, Luis Espinosa Anke, Mohamed Sordo, Horacio Saggion, Xavier Serra
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Entity Linking

In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain. It contains thousands of musical named entities such as Artist, Song or Record Label, which have been automatically annotated on a set of artist biographies coming from the Music website and social network Last.fm. The annotation process relies on the analysis of the hyperlinks present in the source texts and in a voting-based algorithm for EL, which considers, for each entity mention in text, the degree of agreement across three state-of-the-art EL systems. Manual evaluation shows that EL Precision is at least 94{\%}, and due to its tunable nature, it is possible to derive annotations favouring higher Precision or Recall, at will. We make available the annotated dataset along with evaluation data and the code.
Bridge-Language Capitalization Inference in Western Iranian: Sorani, Kurmanji, Zazaki, and Tajik
Authors: Patrick Littell, David R. Mortensen, Kartik Goyal, Chris Dyer, Lori Levin
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

In Sorani Kurdish, one of the most useful orthographic features in named-entity recognition {--} capitalization {--} is absent, as the language{'}s Perso-Arabic script does not make a distinction between uppercase and lowercase letters. We describe a system for deriving an inferred capitalization value from closely related languages by phonological similarity, and illustrate the system using several related Western Iranian languages.
Annotating Named Entities in Consumer Health Questions
Authors: Halil Kilicoglu, Asma Ben Abacha, Yassine Mrabet, Kirk Roberts, Laritza Rodriguez, Sonya Shooshan, Dina Demner-Fushman
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We describe a corpus of consumer health questions annotated with named entities. The corpus consists of 1548 de-identified questions about diseases and drugs, written in English. We defined 15 broad categories of biomedical named entities for annotation. A pilot annotation phase in which a small portion of the corpus was double-annotated by four annotators was followed by a main phase in which double annotation was carried out by six annotators, and a reconciliation phase in which all annotations were reconciled by an expert. We conducted the annotation in two modes, manual and assisted, to assess the effect of automatic pre-annotation and calculated inter-annotator agreement. We obtained moderate inter-annotator agreement; assisted annotation yielded slightly better agreement and fewer missed annotations than manual annotation. Due to complex nature of biomedical entities, we paid particular attention to nested entities for which we obtained slightly lower inter-annotator agreement, confirming that annotating nested entities is somewhat more challenging. To our knowledge, the corpus is the first of its kind for consumer health text and is publicly available.
A Regional News Corpora for Contextualized Entity Discovery and Linking
Authors: Adrian Bra{\c{s}}oveanu, Lyndon J.B. Nixon, Albert Weichselbraun, Arno Scharl
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, Entity Linking, Knowledge Base Population

This paper presents a German corpus for Named Entity Linking (NEL) and Knowledge Base Population (KBP) tasks. We describe the annotation guideline, the annotation process, NIL clustering techniques and conversion to popular NEL formats such as NIF and TAC that have been used to construct this corpus based on news transcripts from the German regional broadcaster RBB (Rundfunk Berlin Brandenburg). Since creating such language resources requires significant effort, the paper also discusses how to derive additional evaluation resources for tasks like named entity contextualization or ontology enrichment by exploiting the links between named entities from the annotated corpus. The paper concludes with an evaluation that shows how several well-known NEL tools perform on the corpus, a discussion of the evaluation results, and with suggestions on how to keep evaluation corpora and datasets up to date.
DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus
Authors: Martin Br{\"u}mmer, Milan Dojchinovski, Sebastian Hellmann
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Entity Linking, Multilingual NLP

The ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora. Due to its size, its openness and relative quality, the Wikipedia has already been a source of such data, but on a limited scale. This paper introduces the DBpedia Abstract Corpus, a large-scale, open corpus of annotated Wikipedia texts in six languages, featuring over 11 million texts and over 97 million entity links. The properties of the Wikipedia texts are being described, as well as the corpus creation process, its format and interesting use-cases, like Named Entity Linking training and evaluation.
Government Domain Named Entity Recognition for South African Languages
Authors: Roald Eiselen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

This paper describes the named entity language resources developed as part of a development project for the South African languages. The development efforts focused on creating protocols and annotated data sets with at least 15,000 annotated named entity tokens for ten of the official South African languages. The description of the protocols and annotated data sets provide an overview of the problems encountered during the annotation of the data sets. Based on these annotated data sets, CRF named entity recognition systems are developed that leverage existing linguistic resources. The newly created named entity recognisers are evaluated, with F-scores of between 0.64 and 0.77, and error analysis is performed to identify possible avenues for improving the quality of the systems.
Named Entity Resources - Overview and Outlook
Authors: Maud Ehrmann, Damien Nouvel, Sophie Rosset
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Entity Linking

Recognition of real-world entities is crucial for most NLP applications. Since its introduction some twenty years ago, named entity processing has undergone a significant evolution with, among others, the definition of new tasks (e.g. entity linking) and the emergence of new types of data (e.g. speech transcriptions, micro-blogging). These pose certainly new challenges which affect not only methods and algorithms but especially linguistic resources. Where do we stand with respect to named entity resources? This paper aims at providing a systematic overview of named entity resources, accounting for qualities such as multilingualism, dynamicity and interoperability, and to identify shortfalls in order to guide future developments.
Incorporating Lexico-semantic Heuristics into Coreference Resolution Sieves for Named Entity Recognition at Document-level
Authors: Marcos Garcia
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: coreference-resolution, Coreference Resolution, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper explores the incorporation of lexico-semantic heuristics into a deterministic Coreference Resolution (CR) system for classifying named entities at document-level. The highest precise sieves of a CR tool are enriched with both a set of heuristics for merging named entities labeled with different classes and also with some constraints that avoid the incorrect merging of similar mentions. Several tests show that this strategy improves both NER labeling and CR. The CR tool can be applied in combination with any system for named entity recognition using the CoNLL format, and brings benefits to text analytics tasks such as Information Extraction. Experiments were carried out in Spanish, using three different NER tools.
Using Word Embeddings to Translate Named Entities
Authors: Octavia-Maria {\c{S}}ulea, Sergiu Nisioi, Liviu P. Dinu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Chinese Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, Translation, Word Embeddings

In this paper we investigate the usefulness of neural word embeddings in the process of translating Named Entities (NEs) from a resource-rich language to a language low on resources relevant to the task at hand, introducing a novel, yet simple way of obtaining bilingual word vectors. Inspired by observations in (Mikolov et al., 2013b), which show that training their word vector model on comparable corpora yields comparable vector space representations of those corpora, reducing the problem of translating words to finding a rotation matrix, and results in (Zou et al., 2013), which showed that bilingual word embeddings can improve Chinese Named Entity Recognition (NER) and English to Chinese phrase translation, we use the sentence-aligned English-French EuroParl corpora and show that word embeddings extracted from a merged corpus (corpus resulted from the merger of the two aligned corpora) can be used to NE translation. We extrapolate that word embeddings trained on merged parallel corpora are useful in Named Entity Recognition and Translation tasks for resource-poor languages.