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LREC 2020 Paper Dissemination (2/10)

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LREC 2020 was not held in Marseille this year and only the Proceedings were published.

The ELRA Board and the LREC 2020 Programme Committee now feel that those papers should be disseminated again, in a thematic-oriented way, shedding light on specific “topics/sessions”.

Packages with several sessions will be disseminated every Tuesday for 10 weeks, from Nov 10, 2020 until the end of January 2021.

Each session displays papers’ title and authors, with corresponding abstract (for ease of reading) and url, in like manner as the Book of Abstracts we used to print and distribute at LRECs.

We hope that you discover interesting, even exciting, work that may be useful for your own research.

Group of papers sent on November 17, 2020

Links to each session

 

 

 
Session Digital Humanities

A Penn-style Treebank of Middle Low German

Hannah Booth, Anne Breitbarth, Aaron Ecay and Melissa Farasyn

We outline the issues and decisions involved in creating a Penn-style treebank of Middle Low German (MLG, 1200-1650), which will form part of the Corpus of Historical Low German (CHLG). The attestation for MLG is rich, but the syntax of the language remains relatively understudied. The development of a syntactically annotated corpus for the language will facilitate future studies with a strong empirical basis, building on recent work which indicates that, syntactically, MLG occupies a position in its own right within West Germanic. In this paper, we describe the background for the corpus and the process by which texts were selected to be included. In particular, we focus on the decisions involved in the syntactic annotation of the corpus, specifically, the practical and linguistic reasons for adopting the Penn annotation scheme, the stages of the annotation process itself, and how we have adapted the Penn scheme for syntactic features specific to MLG. We also discuss the issue of data uncertainty, which is a major issue when building a corpus of an under-researched language stage like MLG, and some novel ways in which we capture this uncertainty in the annotation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.96.pdf

 

Books of Hours. the First Liturgical Data Set for Text Segmentation.

Amir Hazem, Beatrice Daille, Christopher Kermorvant, Dominique Stutzmann, Marie-Laurence Bonhomme, Martin Maarand and Mélodie Boillet

The Book of Hours was the bestseller of the late Middle Ages and Renaissance. It is a historical invaluable treasure, documenting the devotional practices of Christians in the late Middle Ages. Up to now, its textual content has been scarcely studied because of its manuscript nature, its length and its complex  content. At first glance, it  looks too standardized. However, the study of book of hours raises important challenges: (i) in image analysis, its often lavish ornamentation (illegible painted initials, line-fillers, etc.), abbreviated words, multilingualism are difficult to address in Handwritten Text Recognition (HTR);  (ii) its hierarchical entangled structure offers a new field of investigation for text segmentation; (iii) in digital humanities, its textual content gives opportunities for historical analysis. In this paper,  we provide the first corpus of books of hours, which consists of Latin transcriptions of 300 books of hours generated by Handwritten Text Recognition (HTR) - that is like Optical Character Recognition (OCR) but for handwritten and not printed texts. We designed a structural scheme of the book of hours and annotated manually two books of hours according to this scheme. Lastly,  we performed a systematic  evaluation of  the main state of the art text segmentation approaches.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.97.pdf

 

Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millennia

Sergey Zinin and Yang Xu

Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE. The histories are documented in Classical (Literary) Chinese in a corpus of over 20 million characters, suitable for the computational analysis of historical lexicon and semantic change. However, there is no freely available open-source corpus of these histories, making Classical Chinese low-resource. This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license. An original list of Classical Chinese gender-specific terms was developed as a case study for analyzing the historical linguistic use of male and female terms. The study demonstrates considerable stability in the usage of these terms, with dominance of male terms. Exploration of word meanings uses keyword analysis of focus corpora created for gender-specific terms. This method yields meaningful semantic representations that can be used for future studies of diachronic semantics.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.98.pdf

 

The Royal Society Corpus 6.0: Providing 300+ Years of Scientific Writing for Humanistic Study

Stefan Fischer, Jörg Knappen, Katrin Menzel and Elke Teich

We present a new, extended version of the Royal Society Corpus (RSC), a diachronic corpus of scientific English now covering 300+ years of scientific writing (1665–1996). The corpus comprises 47 837 texts, primarily scientific articles, and is based on publications of the Royal Society of London, mainly its Philosophical Transactions and Proceedings. The corpus has been built on the basis of the FAIR principles and is freely available under a Creative Commons license, excluding copy-righted parts. We provide information on how the corpus can be found, the file formats available for download as well as accessibility via a web-based corpus query platform. We show a number of analytic tools that we have implemented for better usability and provide an example of use of the corpus for linguistic analysis as well as examples of subsequent, external uses of earlier releases. We place the RSC against the background of existing English diachronic/scientific corpora, elaborating on its value for linguistic and humanistic study.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.99.pdf

 

Corpus REDEWIEDERGABE

Annelen Brunner, Stefan Engelberg, Fotis Jannidis, Ngoc Duyen Tanja Tu and Lukas Weimer

This article presents corpus REDEWIEDERGABE, a German-language historical corpus with detailed annotations for speech, thought and writing representation (ST&WR). With approximately 490,000 tokens, it is the largest resource of its kind. It can be used to answer literary and linguistic research questions and serve as training material for machine learning. This paper describes the composition of the corpus and the annotation structure, discusses some methodological decisions and gives basic statistics about the forms of ST&WR found in this corpus.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.100.pdf

 

WeDH - a Friendly Tool for Building Literary Corpora Enriched with Encyclopedic Metadata

Mattia Egloff and Davide Picca

In recent years the interest in the use of repositories of literary works has been successful. While many efforts related to Linked Open Data go in the right direction, the use of these repositories for the creation of text corpora enriched with metadata remains difficult and cumbersome. In fact, many of these repositories can be useful to the community not only for the automatic creation of textual corpora but also for retrieving crucial meta-information about texts. In particular, the use of metadata provides the reader with a wealth of information that is often not identifiable in the texts themselves. Our project aims to fill both the access to the textual resources available on the web and the possibility of combining these resources with sources of metadata that can enrich the texts with useful information lengthening the life and maintenance of the data itself. We introduce here a user-friendly web interface of the Digital Humanities toolkit named WeDH with which the user can leverage the encyclopedic knowledge provided by DBpedia, wikidata and VIAF in order to enrich the corpora with bibliographical and exegetical knowledge. WeDH is a collaborative project and we invite anyone who has ideas or suggestions regarding this procedure to reach out to us.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.101.pdf

 

Automatic Section Recognition in Obituaries

Valentino Sabbatino, Laura Ana Maria Bostan and Roman Klinger

Obituaries contain information about people’s values across times and cultures, which makes them a useful resource for exploring cultural history. They are typically structured similarly, with sections corresponding to Personal Information, Biographical Sketch, Characteristics, Family, Gratitude, Tribute, Funeral Information and Other aspects of the person. To make this information available for further studies, we propose a statistical model which recognizes these sections. To achieve that, we collect a corpus of 20058 English obituaries from TheDaily Item, Remembering.CA and The London Free Press. The evaluation of our annotation guidelines with three annotators on 1008 obituaries shows a substantial agreement of Fleiss κ = 0.87. Formulated as an automatic segmentation task, a convolutional neural network outperforms bag-of-words and embedding-based BiLSTMs and BiLSTM-CRFs with a micro F1 = 0.81.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.102.pdf

 

SLäNDa: An Annotated Corpus of Narrative and Dialogue in Swedish Literary Fiction

Sara Stymne and Carin Östman

We describe a new corpus, SLäNDa, the Swedish Literary corpus of Narrative and Dialogue. It contains Swedish literary fiction, which has been manually annotated for cited materials, with a focus on dialogue. The annotation covers excerpts from eight Swedish novels written between 1879--1940, a period of modernization of the Swedish language. SLäNDa contains annotations for all cited materials that are separate from the main narrative, like quotations and signs. The main focus is on dialogue, for which we annotate speech segments, speech tags, and speakers. In this paper we describe the annotation protocol and procedure and show that we can reach a  high inter-annotator agreement. In total, SLäNDa contains annotations of 44 chapters with over 220K tokens. The annotation identified 4,733 instances of cited material and 1,143 named  speaker--speech mappings. The corpus is useful for developing computational tools for different types of analysis of literary narrative and speech. We perform a small pilot study where we show how our annotation can help in analyzing language change in Swedish. We find that a  number of common function words have their modern version appear earlier in speech than in narrative.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.103.pdf

 

RiQuA: A Corpus of Rich Quotation Annotation for English Literary Text

Sean Papay and Sebastian Padó

We introduce RiQuA (RIch QUotation Annotations), a corpus that provides quotations, including their interpersonal structure (speakers and addressees) for English literary text. The corpus comprises 11 works of 19th-century literature that were manually doubly annotated for direct and indirect quotations. For each quotation, its span, speaker, addressee, and cue are identified (if present). This provides a rich view of dialogue structures not available from other available corpora. We detail the process of creating this dataset, discuss the annotation guidelines, and analyze the resulting corpus in terms of inter-annotator agreement and its properties. RiQuA, along with its annotations guidelines and associated scripts, are publicly available for use, modification, and experimentation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.104.pdf

 

A Corpus Linguistic Perspective on Contemporary German Pop Lyrics with the Multi-Layer Annotated "Songkorpus"

Roman Schneider

Song lyrics can be considered as a text genre that has features of both written and spoken discourse, and potentially provides extensive linguistic and cultural information to scientists from various disciplines. However, pop songs play a rather subordinate role in empirical language research so far - most likely due to the absence of scientifically valid and sustainable resources. The present paper introduces a multiply annotated corpus of German lyrics as a publicly available basis for multidisciplinary research. The resource contains three types of data for the investigation and evaluation of quite distinct phenomena: TEI-compliant song lyrics as primary data, linguistically and literary motivated annotations, and extralinguistic metadata. It promotes empirically/statistically grounded analyses of genre-specific features, systemic-structural correlations and tendencies in the texts of contemporary pop music. The corpus has been stratified into thematic and author-specific archives; the paper presents some basic descriptive statistics, as well as the public online frontend with its built-in evaluation forms and live visualisations.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.105.pdf

 

The BDCamões Collection of Portuguese Literary Documents: a Research Resource for Digital Humanities and Language Technology

Sara Grilo, Márcia Bolrinha, João Silva, Rui Vaz and António Branco

This paper presents the BDCamões Collection of Portuguese Literary Documents, a new corpus of literary texts written in Portuguese that in its inaugural version includes close to 4 million words from over 200 complete documents from 83 authors in 14 genres, covering a time span from the 16th to the 21st century, and adhering to different orthographic conventions. Many of the texts in the corpus have also been automatically parsed with state-of-the-art language processing tools, forming the BDCamões Treebank subcorpus. This set of characteristics makes of BDCamões an invaluable resource for research in language technology (e.g. authorship detection, genre classification, etc.) and in language science and digital humanities (e.g. comparative literature, diachronic linguistics, etc.).

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.106.pdf

 

Dataset for Temporal Analysis of English-French Cognates

Esteban Frossard, Mickael Coustaty, Antoine Doucet, Adam Jatowt and Simon Hengchen

Languages change over time and, thanks to the abundance of digital corpora, their evolutionary analysis using computational techniques has recently gained much research attention. In this paper, we focus on creating a dataset to support investigating the similarity in evolution between different languages. We look in particular into the similarities and differences between the use of corresponding words across time in English and French, two languages from different linguistic families yet with shared syntax and close contact. For this we select a set of cognates in both languages and study their frequency changes and correlations over time. We propose a new dataset for computational approaches of synchronized diachronic investigation of language pairs, and subsequently show novel findings stemming from the cognate-focused diachronic comparison of the two chosen languages. To the best of our knowledge, the present study is the first in the literature to use computational approaches and large data to make a cross-language diachronic analysis.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.107.pdf

 

Material Philology Meets Digital Onomastic Lexicography: The NordiCon Database of Medieval Nordic Personal Names in Continental Sources

Michelle Waldispühl, Dana Dannells and Lars Borin

We present NordiCon, a database containing medieval Nordic personal names attested in Continental sources. The database combines formally interpreted and richly interlinked onomastic data with digitized versions of the medieval manuscripts from which the data originate and information on the tokens' context. The structure of NordiCon is inspired by other online historical given name dictionaries. It takes up challenges reported on in previous works, such as how to cover material properties of a name token and how to define lemmatization principles, and elaborates on possible solutions. The lemmatization principles for NordiCon are further developed in order to facilitate the connection to other name dictionaries and corpuses, and the integration of the database into Språkbanken Text, an infrastructure containing modern and historical written data.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.108.pdf

 

NLP Scholar: A Dataset for Examining the State of NLP Research

Saif M. Mohammad

Google Scholar is the largest web search engine for academic literature that also provides access to rich metadata associated with the papers. The ACL Anthology (AA) is the largest repository of articles on Natural Language Processing (NLP). We extracted information from AA for about 44 thousand NLP papers and identified authors who published at least three papers there. We then extracted citation information from Google Scholar for all their papers (not just their AA papers). This resulted in a dataset of 1.1 million papers and associated Google Scholar information. We aligned the information in the AA and Google Scholar datasets to create the NLP Scholar Dataset -- a single unified source of information (from both AA and Google Scholar) for tens of thousands of NLP papers. It can be used to identify broad trends in productivity, focus, and impact of NLP research. We present here initial work on analyzing the volume of  research in NLP over the years and identifying the most cited papers in NLP. We also list a number of additional potential applications.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.109.pdf

 

The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages

Shafqat Mumtaz Virk, Harald Hammarström, Markus Forsberg and Søren Wichmann

There exist as many as 7000 natural languages in the world, and a huge number of documents describing those languages have been produced over the years. Most of those documents are in paper format. Any attempts to use modern computational techniques and tools to process those documents will require them to be digitized first. In this paper, we report a multilingual digitized version of thousands of such documents searchable through some well-established corpus infrastructures. The corpus is annotated with various meta, word, and text level attributes to make searching and analysis easier and more useful.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.110.pdf

 

LiViTo: Linguistic and Visual Features Tool for Assisted Analysis of Historic Manuscripts

Klaus Müller, Aleksej Tikhonov and Roland Meyer

We propose a mixed methods approach to the identification of scribes and authors in handwritten documents, and present LiViTo, a software tool which combines linguistic insights and computer vision techniques in order to assist researchers in the analysis of handwritten historical documents. Our research shows that it is feasible to train neural networks for the automatic transcription of handwritten documents and to use these transcriptions as input for further learning processes. Hypotheses about scribes can be tested effectively by extracting visual handwriting features and clustering them appropriately. Methods from linguistics and from computer vision research integrate into a mixed methods system, with benefits on both sides. LiViTo was trained with historical Czech texts by 18th century immigrants to Berlin, a total of 564 pages from a corpus of about 5000 handwritten pages without indication of author or scribe. We provide an overview of the three-year development of LiViTo and an introduction into its methodology and its functions. We then present our findings concerning the corpus of Berlin Czech manuscripts and discuss possible further usage scenarios.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.111.pdf

 

TextAnnotator: A UIMA Based Tool for the Simultaneous and Collaborative Annotation of Texts

Giuseppe Abrami, Manuel Stoeckel and Alexander Mehler

The annotation of texts and other material in the field of digital humanities and Natural Language Processing (NLP) is a common task of research projects. At the same time, the annotation of corpora is certainly the most time- and cost-intensive component in research projects and often requires a high level of expertise according to the research interest. However, for the annotation of texts, a wide range of tools is available, both for automatic and manual annotation. Since the automatic pre-processing methods are not error-free and there is an increasing demand for the generation of training data, also with regard to machine learning, suitable annotation tools are required. This paper defines criteria of flexibility and efficiency of complex annotations for the assessment of existing annotation tools. To extend this list of tools, the paper describes TextAnnotator, a browser-based, multi-annotation system, which has been developed to perform platform-independent multimodal annotations and annotate complex textual structures. The paper illustrates the current state of development of TextAnnotator and demonstrates its ability to evaluate annotation quality (inter-annotator agreement) at runtime. In addition, it will be shown how annotations of different users can be performed simultaneously and collaboratively on the same document from different platforms using UIMA as the basis for annotation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.112.pdf

 

Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings

Bikash Gyawali, Lucas Anastasiou and Petr Knoth

Deduplication is the task of identifying near and exact duplicate data items in a collection. In this paper, we present a novel method for deduplication of scholarly documents. We develop a hybrid model which uses structural similarity (locality sensitive hashing) and meaning representation (word embeddings) of document texts to determine (near) duplicates. Our collection constitutes a subset of multidisciplinary scholarly documents aggregated from research  repositories. We identify several issues causing data inaccuracies in such collections and motivate the need for deduplication. In lack of existing dataset suitable for study of deduplication of scholarly documents, we create a ground truth dataset of $100K$ scholarly documents and conduct a series of experiments to empirically establish optimal values for the parameters of our deduplication method. Experimental evaluation shows that our method achieves a macro F1-score of 0.90. We productionise our method as a publicly accessible web API service serving deduplication of scholarly documents in real time.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.113.pdf

 

 “Voices of the Great War”: A Richly Annotated Corpus of Italian Texts on the First World War

Federico Boschetti, irene de felice, Stefano Dei Rossi, Felice Dell’Orletta, Michele Di Giorgio, Martina Miliani, Lucia C. Passaro, Angelica Puddu, Giulia Venturi, Nicola Labanca, Alessandro Lenci and Simonetta Montemagni

“Voices of the Great War” is the first large corpus of Italian historical texts dating back to the period of First World War. This corpus differs from other existing resources in several respects. First, from the linguistic point of view it gives account of the wide range of varieties in which Italian was articulated in that period, namely from a diastratic (educated vs. uneducated writers), diaphasic (low/informal vs. high/formal registers) and diatopic (regional varieties, dialects) points of view. From the historical perspective, through a collection of texts belonging to different genres it represents different views on the war and the various styles of narrating war events and experiences. The final corpus is balanced along various dimensions, corresponding to the textual genre, the language variety used, the author type and the typology of conveyed contents. The corpus is fully annotated with lemmas, part-of-speech, terminology, and named entities. Significant corpus samples representative of the different “voices” have also been enriched with meta-linguistic and syntactic information. The layer of syntactic annotation forms the first nucleus of an Italian historical treebank complying with the Universal Dependencies standard. The paper illustrates the final resource, the methodology and tools used to build it, and the Web Interface for navigating it.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.114.pdf

 

DEbateNet-mig15:Tracing the 2015 Immigration Debate in Germany Over Time

Gabriella Lapesa, Andre Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, Jonas Kuhn and Sebastian Padó

DEbateNet-migr15 is a manually annotated dataset for German which covers the public debate on immigration in 2015. The building block of our annotation is the political science notion of a claim, i.e., a statement made by a political actor (a politician, a party, or a group of citizens) that a specific action should be taken (e.g., vacant flats should be assigned to refugees). We identify claims in newspaper articles, assign them to actors and fine-grained categories and annotate their polarity and date. The aim of this paper is two-fold: first, we release the full DEbateNet-mig15 corpus and document it by means of a quantitative and qualitative analysis; second, we demonstrate its application in a discourse network analysis framework, which enables us to capture the temporal dynamics of the political debate

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.115.pdf

 

A Corpus of Spanish Political Speeches from 1937 to 2019

Elena Álvarez-Mellado

This paper documents a corpus of political speeches in Spanish. The documents in the corpus belong to the Christmas speeches that have been delivered yearly by the head of state of Spain since 1937. The historical period covered by these speeches ranges from the Spanish Civil War and the Francoist dictatorship up until today. As a result, the corpus reflects some of the most significant events and political changes in the recent history of Spain. Up until now, the speeches as a whole had not been collected into a single, systematic and reusable resource, as most of the texts were scattered among different sources. The paper describes: (1) the composition of the corpus; (2) the Python interface that facilitates querying and analyzing the corpus using the NLTK and spaCy libraries and (3) a set of HTML visualizations aimed at the general public to navigate the corpus and explore differences between TF-IDF frequencies.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.116.pdf

 

A New Latin Treebank for Universal Dependencies: Charters between Ancient Latin and Romance Languages

Flavio Massimiliano Cecchini, Timo Korkiakangas and Marco Passarotti

The present work introduces a new Latin treebank that follows the Universal Dependencies (UD) annotation standard. The treebank is obtained from the automated conversion of the Late Latin Charter Treebank 2 (LLCT2), originally in the Prague Dependency Treebank (PDT) style. As this treebank consists of Early Medieval legal documents, its language variety differs considerably from both the Classical and Medieval learned varieties prevalent in the other currently available UD Latin treebanks. Consequently, besides significant phenomena from the perspective of diachronic linguistics, this treebank also poses several challenging technical issues for the current and future syntactic annotation of Latin in the UD framework. Some of the most relevant cases are discussed in depth, with comparisons between the original PDT and the resulting UD annotations. Additionally, an overview of the UD-style structure of the treebank is given, and some diachronic aspects of the transition from Latin to Romance languages are highlighted.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.117.pdf

 

Identification of Indigenous Knowledge Concepts through Semantic Networks, Spelling Tools and Word Embeddings

Renato Rocha Souza, Amelie Dorn, Barbara Piringer and Eveline Wandl-Vogt

In order to access indigenous, regional knowledge contained in language corpora, semantic tools and network methods are most typically employed. In this paper we present an approach for the identification of dialectal variations of words, or words that do not pertain to High German, on the example of non-standard language legacy collection questionnaires of the Bavarian Dialects in Austria (DBÖ). Based on selected cultural categories relevant to the wider project context, common words from each of these cultural categories and their lemmas using GermaLemma were identified. Through word embedding models the semantic vicinity of each word was explored, followed by the use of German Wordnet (Germanet) and the Hunspell tool. Whilst none of these tools have a comprehensive coverage of standard German words, they serve as an indication of dialects in specific semantic hierarchies. Methods and tools applied in this study may serve as an example for other similar projects dealing with non-standard or endangered language collections, aiming to access, analyze and ultimately preserve native regional language heritage.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.118.pdf

 

A Multi-Orthography Parallel Corpus of Yiddish Nouns

Jonne Saleva

Yiddish is a low-resource language belonging to the Germanic language family and written using the Hebrew alphabet. As a language, Yiddish can be considered resource-poor as it lacks both public accessible corpora and a widely-used standard orthography, with various countries and organizations influencing the spellings speakers use. While existing corpora of Yiddish text do exist, they are often only written in a single, potentially non-standard orthography, with no parallel version with standard orthography available. In this work, we introduce the first multi-orthography parallel corpus of Yiddish nouns built by scraping word entries from Wiktionary. We also demonstrate how the corpus can be used to bootstrap a transliteration model using the Sequitur-G2P grapheme-to-phoneme conversion toolkit to map between various orthographies. Our trained system achieves error rates between 16.79% and 28.47% on the test set, depending on the orthographies considered. In addition to quantitative analysis, we also conduct qualitative error analysis of the trained system, concluding that non-phonetically spelled Hebrew words are the largest cause of error. We conclude with remarks regarding future work and release the corpus and associated code under a permissive license for the larger community to use.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.119.pdf

 

An Annotated Corpus of Adjective-Adverb Interfaces in Romance Languages

Katharina Gerhalter, Gerlinde Schneider, Christopher Pollin and Martin Hummel

The final outcome of the project Open Access Database: Adjective-Adverb Interfaces in Romance is an annotated and lemmatised corpus of various linguistic phenomena related to Romance adjectives with adverbial functions. The data is published under open-access and aims to serve linguistic research based on transparent and accessible corpus-based data. The annotation model was developed to offer a cross-linguistic categorization model for the heterogeneous word-class “adverb”, based on its diverse forms, functions and meanings. The project focuses on the interoperability and accessibility of data, with particular respect to reusability in the sense of the FAIR Data Principles. Topics presented by this paper include data compilation and creation, annotation in XML/TEI, data preservation and publication process by means of the GAMS repository and accessibility via a search interface. These aspects are tied together by semantic technologies, using an ontology-based approach.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.120.pdf

 

Language Resources for Historical Newspapers: the Impresso Collection

Maud Ehrmann, Matteo Romanello, Simon Clematide, Phillip Benjamin Ströbel and Raphaël Barman

Following decades of massive digitization, an unprecedented amount of historical document facsimiles can now be retrieved and accessed via cultural heritage online portals. If this represents a huge step forward in terms of preservation and accessibility, the next fundamental challenge-- and real promise of digitization-- is to exploit the contents of these digital assets, and therefore to adapt and develop appropriate language technologies to search and retrieve information from this `Big Data of the Past'. Yet, the application of text processing tools on historical documents in general, and historical newspapers in particular, poses new challenges, and crucially requires appropriate language resources. In this context, this paper presents a collection of historical newspaper data sets composed of text and image resources, curated and published within the context of the `impresso - Media Monitoring of the Past' project. With corpora, benchmarks, semantic annotations and language models in French, German and Luxembourgish covering ca. 200 years, the objective of the impresso resource collection is to contribute to historical language resources, and thereby strengthen the robustness of approaches to non-standard inputs and foster efficient processing of historical documents.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.121.pdf

 

Allgemeine Musikalische Zeitung as a Searchable Online Corpus

Bernd Kampe, Tinghui Duan and Udo Hahn

The massive digitization efforts related to historical newspapers over the past decades have focused on mass media sources and ordinary people as their primary recipients. Much less attention has been paid to newspapers published for a more specialized audience, e.g., those aiming at scholarly or cultural exchange within intellectual communities much narrower in scope, such as newspapers devoted to music criticism, arts or philosophy. Only some few of these specialized newspapers have been digitized up until now, but they are usually not well curated in terms of digitization quality, data formatting, completeness, redundancy (de-duplication), supply of metadata, and, hence, searchability. This paper describes our approach to eliminate these drawbacks for a major German-language newspaper resource of the Romantic Age, the Allgemeine Musikalische Zeitung (General Music Gazette). We here focus on a workflow that copes with a posteriori digitization problems, inconsistent OCRing and index building for searchability. In addition, we provide a user-friendly graphic interface to empower content-centric access to this (and other) digital resource(s) adopting open-source software for the purpose of Web presentation.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.122.pdf

 

Stylometry in a Bilingual Setup

Silvie Cinkova and Jan Rybicki

The method of stylometry by most frequent words does not allow direct comparison of original texts and their translations, i.e. across languages. For instance, in a bilingual Czech-German text collection containing parallel texts (originals and translations in both directions, along with Czech and German translations from other languages), authors would not cluster across languages, since frequency word lists for any Czech texts are obviously going to be more similar to each other than to a German text, and the other way round. We have tried to come up with an interlingua that would remove the language-specific features and possibly keep the linguistically independent features of individual author signal, if they exist. We have tagged, lemmatized, and parsed each language counterpart with the corresponding language model in UDPipe, which provides a linguistic markup that is cross-lingual to a significant extent. We stripped the output of language-dependent items, but that alone did not help much. As a next step, we transformed the lemmas of both language counterparts into shared pseudolemmas based on a very crude Czech-German glossary, with a 95.6% success. We show that, for stylometric methods based on the most frequent words, we can do without translations.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.123.pdf

 

Dialect Clustering with Character-Based Metrics: in Search of the Boundary of Language and Dialect

Yo Sato and Kevin Heffernan

We present in this work a universal, character-based method for representing sentences so that one can thereby calculate the distance between any two sentence pair. With a small alphabet, it can function as a proxy of phonemes, and as one of its main uses, we carry out dialect clustering: cluster a dialect/sub-language mixed corpus into sub-groups and see if they coincide with the conventional boundaries of dialects and sub-languages. By using data with multiple Japanese dialects and multiple Slavic languages, we report how well each group clusters, in a manner to partially respond to the question of what separates languages from dialects.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.124.pdf

 

Session Discourse Annotation, Representation and Processing

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DiscSense: Automated Semantic Analysis of Discourse Markers

Damien Sileo, Tim Van de Cruys, Camille Pradel and Philippe Muller

Using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets). These predictions allow us to study the link between discourse markers and the semantic relations annotated in classification datasets. Handcrafted mappings have been proposed between markers and discourse relations on a limited set of markers and a limited set of categories, but there exists hundreds of discourse markers expressing a wide variety of relations, and there is no consensus on the taxonomy of relations between competing discourse theories (which are largely built in a top-down fashion). By using an automatic prediction method over existing semantically annotated datasets, we provide a bottom-up characterization of discourse markers in English. The resulting dataset, named DiscSense, is publicly available.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.125.pdf

 

ThemePro: A Toolkit for the Analysis of Thematic Progression

Monica Dominguez, Juan Soler and Leo Wanner

This paper introduces ThemePro, a toolkit for the automatic analysis of thematic progression. Thematic progression is relevant to natural language processing (NLP) applications dealing, among others, with discourse structure, argumentation structure, natural language generation, summarization and topic detection. A web platform demonstrates the potential of this toolkit and provides a visualization of the results including syntactic trees, hierarchical thematicity over propositions and thematic progression over whole texts.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.126.pdf

 

Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation

Yohan Jo, Elijah Mayfield, Chris Reed and Eduard Hovy

We introduce a corpus of the 2016 U.S. presidential debates and commentary, containing 4,648 argumentative propositions annotated with fine-grained proposition types. Modern machine learning pipelines for analyzing argument have difficulty distinguishing between types of propositions based on their factuality, rhetorical positioning, and speaker commitment. Inability to properly account for these facets leaves such systems inaccurate in understanding of fine-grained proposition types. In this paper, we demonstrate an approach to annotating for four complex proposition types, namely normative claims, desires, future possibility, and reported speech. We develop a hybrid machine learning and human workflow for annotation that allows for efficient and reliable annotation of complex linguistic phenomena, and demonstrate with preliminary analysis of rhetorical strategies and structure in presidential debates. This new dataset and method can support technical researchers seeking more nuanced representations of argument, as well as argumentation theorists developing new quantitative analyses.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.127.pdf

 

Chinese Discourse Parsing: Model and Evaluation

Lin Chuan-An, Shyh-Shiun Hung, Hen-Hsen Huang and Hsin-Hsi Chen

Chinese discourse parsing, which aims to identify the hierarchical relationships of Chinese elementary discourse units, has not yet a consistent evaluation metric. Although Parseval is commonly used, variations of evaluation differ from three aspects: micro vs. macro F1 scores, binary vs. multiway ground truth, and left-heavy vs. right-heavy binarization. In this paper, we first propose a neural network model that unifies a pre-trained transformer and CKY-like algorithm, and then compare it with the previous models with different evaluation scenarios. The experimental results show that our model outperforms the previous systems. We conclude that (1) the pre-trained context embedding provides effective solutions to deal with implicit semantics in Chinese texts, and (2) using multiway ground truth is helpful since different binarization approaches lead to significant differences in performance.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.128.pdf

 

Shallow Discourse Annotation for Chinese TED Talks

Wanqiu Long, Xinyi Cai, James Reid, Bonnie Webber and Deyi Xiong

Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.129.pdf

 

The Discussion Tracker Corpus of Collaborative Argumentation

Christopher Olshefski, Luca Lugini, Ravneet Singh, Diane Litman and Amanda Godley

Although NLP research on argument mining has advanced considerably in recent years, most studies draw on corpora of asynchronous and written texts, often produced by individuals. Few published corpora of synchronous, multi-party argumentation are available. The Discussion Tracker corpus, collected in high school English classes, is an annotated dataset of transcripts of spoken, multi-party argumentation. The corpus consists of 29 multi-party discussions of English literature transcribed from 985 minutes of audio. The transcripts were annotated for three dimensions of collaborative argumentation: argument moves (claims, evidence, and explanations), specificity (low, medium, high) and collaboration (e.g., extensions of and disagreements about others' ideas). In addition to providing descriptive statistics on the corpus,  we provide performance benchmarks and associated code for predicting each dimension separately,   illustrate the use of the multiple annotations in the corpus to improve performance via multi-task learning, and finally discuss other ways the corpus might be used to further NLP research.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.130.pdf

 

Shallow Discourse Parsing for Under-Resourced Languages: Combining Machine Translation and Annotation Projection

Henny Sluyter-Gäthje, Peter Bourgonje and Manfred Stede

Shallow Discourse Parsing (SDP), the identification of coherence relations between text spans, relies on large amounts of training data, which so far exists only for English - any other language is in this respect an under-resourced one. For those languages where machine translation from English is available with reasonable quality, MT in conjunction with annotation projection can be an option for producing an SDP resource. In our study, we translate the English Penn Discourse TreeBank into German and experiment with various methods of annotation projection to arrive at the German counterpart of the PDTB. We describe the key characteristics of the corpus as well as some typical sources of errors encountered during its creation. Then we evaluate the GermanPDTB by training components for selected sub-tasks of discourse parsing on this silver data and compare performance to the same components when trained on the gold, original PDTB corpus.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.131.pdf

 

A Corpus of Encyclopedia Articles with Logical Forms

Nathan Rasmussen and William Schuler

People can extract precise, complex logical meanings from text in documents such as tax forms and game rules, but language processing systems lack adequate training and evaluation resources to do these kinds of tasks reliably. This paper describes a corpus of annotated typed lambda calculus translations for approximately 2,000 sentences in Simple English Wikipedia, which is assumed to constitute a broad-coverage domain for precise, complex descriptions. The corpus described in this paper contains a large number of quantifiers and interesting scoping configurations, and is presented specifically as a resource for quantifier scope disambiguation systems, but also more generally as an object of linguistic study.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.132.pdf

 

The Potsdam Commentary Corpus 2.2: Extending Annotations for Shallow Discourse Parsing

Peter Bourgonje and Manfred Stede

We present the Potsdam Commentary Corpus 2.2, a German corpus of news editorials annotated on several different levels. New in the 2.2 version of the corpus are two additional annotation layers for coherence relations following the Penn Discourse TreeBank framework. Specifically, we add relation senses to an already existing layer of discourse connectives and their arguments, and we introduce a new layer with additional coherence relation types, resulting in a German corpus that mirrors the PDTB. The aim of this is to increase usability of the corpus for the task of shallow discourse parsing. In this paper, we provide inter-annotator agreement figures for the new annotations and compare corpus statistics based on the new annotations to the equivalent statistics extracted from the PDTB.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.133.pdf

 

On the Creation of a Corpus for Coherence Evaluation of Discursive Units

Elham Mohammadi, Timothe Beiko and Leila Kosseim

In this paper, we report on our experiments towards the creation of a corpus for coherence evaluation. Most corpora for textual coherence evaluation are composed of randomly shuffled sentences that focus on sentence ordering, regardless of whether the sentences were originally related by a discourse relation. To the best of our knowledge, no publicly available corpus has been designed specifically for the evaluation of coherence of known discursive units. In this paper, we focus on coherence modeling at the intra-discursive level and describe our approach to build a corpus of incoherent pairs of sentences. We experimented with a variety of corruption strategies to create synthetic incoherent pairs of discourse arguments from coherent ones. Using discourse argument pairs from the Penn Discourse Tree Bank, we generate incoherent discourse argument pairs, by swapping either their discourse connective or a discourse argument. To evaluate how incoherent the generated corpora are, we use a convolutional neural network to try to distinguish the original pairs from the corrupted ones. Results of the classifier as well as a manual inspection of the corpora show that generating such corpora is still a challenge as the generated instances are clearly not ``incoherent enough'', indicating that more effort should be spent on developing more robust ways of generating incoherent corpora.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.134.pdf

 

Joint Learning of Syntactic Features Helps Discourse Segmentation

Takshak Desai, Parag Pravin Dakle and Dan Moldovan

This paper describes an accurate framework for carrying out multi-lingual discourse segmentation with BERT (Devlin et al., 2019). The model is trained to identify segments by casting the problem as a token classification problem and jointly learning syntactic features like part-of-speech tags and dependency relations. This leads to significant improvements in performance. Experiments are performed in different languages, such as English, Dutch, German, Portuguese Brazilian and Basque to highlight the cross-lingual effectiveness of the segmenter. In particular, the model achieves a state-of-the-art F-score of 96.7 for the RST-DT corpus (Carlson et al., 2003) improving on the previous best model by 7.2%. Additionally, a qualitative explanation is provided for how proposed changes contribute to model performance by analyzing errors made on the test data.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.135.pdf

 

Creating a Corpus of Gestures and Predicting the Audience Response based on Gestures in Speeches of Donald Trump

Verena Ruf and Costanza Navarretta

Gestures are an important component of non–verbal communication. This has an increasing potential in human–computer interaction. For example, Navarretta (2017b) uses sequences of speech and pauses together with co–speech gestures produced by Barack Obama in order to predict audience response, such as applause. The aim of this study is to explore the role of speech pauses and gestures alone as predictors of audience reaction without other types of speech information.  For this work, we created a corpus of speeches held by Donald Trump before and during his time as president between 2016 and 2019. The data were transcribed with pause information and co–speech gestures were annotated as well as audience responses. Gestures and long silent pauses of the duration of at least 0.5 seconds are the input of computational models to predict audience reaction.  The results of this study indicate that especially head movements and facial expressions play an important role and they confirm that gestures can to some extent be used to predict audience reaction independently of speech.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.136.pdf

 

GeCzLex: Lexicon of Czech and German Anaphoric Connectives

Lucie Poláková, Kateřina Rysová, Magdaléna Rysová and Jiří Mírovský

We introduce the first version of GeCzLex, an online electronic resource for translation equivalents of Czech and German discourse connectives. The lexicon is one of the outcomes of the research on anaphoricity and long-distance relations in discourse, it contains at present anaphoric connectives (ACs) for Czech and German connectives, and further their possible translations documented in bilingual parallel corpora (not necessarily anaphoric). As a basis, we use two existing monolingual lexicons of connectives: the Lexicon of Czech Discourse Connectives (CzeDLex) and the Lexicon of Discourse Markers (DiMLex) for German, interlink their relevant entries via semantic annotation of the connectives (according to the PDTB 3 sense taxonomy) and statistical information of translation possibilities from the Czech and German parallel data of the InterCorp project. The lexicon is, as far as we know, the first bilingual inventory of connectives with linkage on the level of individual entries, and a first attempt to systematically describe devices engaged in long-distance, non-local discourse coherence. The lexicon is freely available under the Creative Commons License.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.137.pdf

 

DiMLex-Bangla: A Lexicon of Bangla Discourse Connectives

Debopam Das, Manfred Stede, Soumya Sankar Ghosh and Lahari Chatterjee

We present DiMLex-Bangla, a newly developed lexicon of discourse connectives in Bangla. The lexicon, upon completion of its first version, contains 123 Bangla connective entries, which are primarily compiled from the linguistic literature and translation of English discourse connectives. The lexicon compilation is later augmented by adding more connectives from a currently developed corpus, called the Bangla RST Discourse Treebank (Das and Stede, 2018). DiMLex-Bangla provides information on syntactic categories of Bangla connectives, their discourse semantics and non-connective uses (if any). It uses the format of the German connective lexicon DiMLex (Stede and Umbach, 1998), which provides a cross-linguistically applicable XML schema. The resource is the first of its kind in Bangla, and is freely available for use in studies on discourse structure and computational applications.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.138.pdf

 

Semi-Supervised Tri-Training for Explicit Discourse Argument Expansion

Rene Knaebel and Manfred Stede

This paper describes a novel application of semi-supervision for shallow discourse parsing. We use a neural approach for sequence tagging and focus on the extraction of explicit discourse arguments. First, additional unlabeled data is prepared for semi-supervised learning. From this data, weak annotations are generated in a first setting and later used in another setting to study performance differences. In our studies, we show an increase in the performance of our models that ranges between 2-10% F1 score. Further, we give some insights to the generated discourse annotations and compare the developed additional relations with the training relations. We release this new dataset of explicit discourse arguments to enable the training of large statistical models.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.139.pdf

 

WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts

Dhivya Chinnappa, Alexis Palmer and Eduardo Blanco

This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-known artifacts paintings, diamonds, and archaeological artifacts), producing 799 artifact-possessor relations with associated attributes. For each article, we also produce a full possession timeline. The full version of the task combines straightforward entity-relation extraction with complex temporal reasoning, as well as verification of textual support for the relevant types of knowledge. Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline. In addition to the corpus, we release evaluation scripts and a baseline model for the task.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.140.pdf

 

TED-Q: TED Talks and the Questions they Evoke

Matthijs Westera, Laia Mayol and Hannah Rohde

We present a new dataset of TED-talks annotated with the questions they evoke and, where available, the answers to these questions. Evoked questions represent a hitherto mostly unexplored type of linguistic data, which promises to open up important new lines of research, especially related to the Question Under Discussion (QUD)-based approach to discourse structure. In this paper we introduce the method and open the first installment of our data to the public. We summarize and explore the current dataset, illustrate its potential by providing new evidence for the relation between predictability and implicitness -- capitalizing on the already existing PDTB-style annotations for the texts we use -- and outline its potential for future research. The dataset should be of interest, at its current scale, to researchers on formal and experimental pragmatics, discourse coherence, information structure, discourse expectations and processing. Our data-gathering procedure is designed to scale up, relying on crowdsourcing by non-expert annotators, with its utility for Natural Language Processing in mind (e.g., dialogue systems, conversational question answering).

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.141.pdf

 

CzeDLex 0.6 and its Representation in the PML-TQ

Jiří Mírovský, Lucie Poláková and Pavlína Synková

CzeDLex is an electronic lexicon of Czech discourse connectives with its data coming from a large treebank annotated with discourse relations. Its new version CzeDLex 0.6 (as compared with the previous version 0.5, which was published in 2017) is significantly larger with respect to manually processed entries. Also, its structure has been modified to allow for primary connectives to appear with multiple entries for a single discourse sense. The lexicon comes in several formats, being both human and machine readable, and is available for searching in PML Tree Query, a user-friendly and powerful search tool for all kinds of linguistically annotated treebanks. The main purpose of this paper/demo is to present the new version of the lexicon and to demonstrate possibilities of mining various types of information from the lexicon using PML Tree Query; we present several examples of search queries over the lexicon data along with their results. The new version of the lexicon, CzeDLex~0.6, is available on-line and was officially released in December 2019 under the Creative Commons License.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.142.pdf

 

Corpus for Modeling User Interactions in Online Persuasive Discussions

Ryo Egawa, Gaku Morio and Katsuhide Fujita

Persuasions are common in online arguments such as discussion forums. To analyze persuasive strategies, it is important to understand how individuals construct posts and comments based on the semantics of the argumentative components. In addition to understanding how we construct arguments, understanding how a user post interacts with other posts (i.e., argumentative inter-post relation) still remains a challenge. Therefore, in this study, we developed a novel annotation scheme and corpus that capture both user-generated inner-post arguments and inter-post relations between users in ChangeMyView, a persuasive forum. Our corpus consists of arguments with 4612 elementary units (EUs) (i.e., propositions), 2713 EU-to-EU argumentative relations, and 605 inter-post argumentative relations in 115 threads. We analyzed the annotated corpus to identify the characteristics of online persuasive arguments, and the results revealed persuasive documents have more claims than non-persuasive ones and different interaction patterns among persuasive and non-persuasive documents. Our corpus can be used as a resource for analyzing persuasiveness and training an argument mining system to identify and extract argument structures. The annotated corpus and annotation guidelines have been made publicly available.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.143.pdf

 

Simplifying Coreference Chains for Dyslexic Children

Rodrigo Wilkens and Amalia Todirascu

We present a work aiming to generate adapted content for dyslexic children for French, in the context of the ALECTOR project. Thus, we developed a system to transform the texts at the discourse level. This system modifies the coreference chains, which are markers of text cohesion, by using rules. These rules were designed following a careful study of coreference chains in both original texts and its simplified versions. Moreover, in order to define reliable transformation rules, we analysed several coreference properties as well as the concurrent simplification operations in the aligned texts. This information is coded together with a coreference resolution system and a text rewritten tool in the proposed system, which comprise a coreference module specialised in written text and seven text transformation operations. The evaluation of the system firstly focused on check the simplification by manual validation of three judges. These errors were grouped into five classes that combined can explain 93% of the errors. The second evaluation step consisted of measuring the simplification perception by 23 judges, which allow us to measure the simplification impact of the proposed rules.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.144.pdf

 

Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives

Yudai Kishimoto, Yugo Murawaki and Sadao Kurohashi

BERT, a neural network-based language model pre-trained on large corpora, is a breakthrough in natural language processing, significantly outperforming previous state-of-the-art models in numerous tasks. However, there have been few reports on its application to implicit discourse relation classification, and it is not clear how BERT is best adapted to the task. In this paper, we test three methods of adaptation. (1) We perform additional pre-training on text tailored to discourse classification. (2) In expectation of knowledge transfer from explicit discourse relations to implicit discourse relations, we add a task named explicit connective prediction at the additional pre-training step. (3) To exploit implicit connectives given by treebank annotators, we add a task named implicit connective prediction at the fine-tuning step. We demonstrate that these three techniques can be combined straightforwardly in a single training pipeline. Through comprehensive experiments, we found that the first and second techniques provide additional gain while the last one did not.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.145.pdf

 

What Speakers really Mean when they Ask Questions: Classification of Intentions with a Supervised Approach

Angèle Barbedette and Iris Eshkol-Taravella

This paper focuses on the automatic detection of hidden intentions of speakers in questions asked during meals. Our corpus is composed of a set of transcripts of spontaneous oral conversations from ESLO's corpora. We suggest a typology of these intentions based on our research work and the exploration and annotation of the corpus, in which we define two "explicit" categories (request for agreement and request for information) and three "implicit" categories (opinion, will and doubt).  We implement a supervised automatic classification model based on annotated data and selected linguistic features and we evaluate its results and performances. We finally try to interpret these results by looking more deeply and specifically into the predictions of the algorithm and the features it used. There are many motivations for this work which are part of ongoing challenges such as opinion analysis, irony detection or the development of conversational agents.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.146.pdf

 

Modeling Dialogue in Conversational Cognitive Health Screening Interviews

Shahla Farzana, Mina Valizadeh and Natalie Parde

Automating straightforward clinical tasks can reduce workload for healthcare professionals, increase accessibility for geographically-isolated patients, and alleviate some of the economic burdens associated with healthcare. A variety of preliminary screening procedures are potentially suitable for automation, and one such domain that has remained underexplored to date is that of structured clinical interviews.  A task-specific dialogue agent is needed to automate the collection of conversational speech for further (either manual or automated) analysis, and to build such an agent, a dialogue manager must be trained to respond to patient utterances in a manner similar to a human interviewer.  To facilitate the development of such an agent, we propose an annotation schema for assigning dialogue act labels to utterances in patient-interviewer conversations collected as part of a clinically-validated cognitive health screening task.  We build a labeled corpus using the schema, and show that it is characterized by high inter-annotator agreement.  We establish a benchmark dialogue act classification model for the corpus, thereby providing a proof of concept for the proposed annotation schema.  The resulting dialogue act corpus is the first such corpus specifically designed to facilitate automated cognitive health screening, and lays the groundwork for future exploration in this area.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.147.pdf

 

Stigma Annotation Scheme and Stigmatized Language Detection in Health-Care Discussions on Social Media

Nadiya Straton, Hyeju Jang and Raymond Ng

Much research has been done within the social sciences on the interpretation and influence of stigma on human behaviour and health, which result in out-of-group exclusion, distancing, cognitive separation, status loss, discrimination, in-group pressure, and often lead to disengagement, non-adherence to treatment plan, and prescriptions by the doctor. However, little work has been conducted on computational identification of stigma in general and in social media discourse in particular. In this paper, we develop the annotation scheme and improve the annotation process for stigma identification, which can be applied to other health-care domains. The data from pro-vaccination and anti-vaccination discussion groups are annotated by trained annotators who have professional background in social science and health-care studies, therefore the group can be considered experts on the subject in comparison to non-expert crowd. Amazon MTurk annotators is another group of annotator with no knowledge on their education background, they are initially treated as non-expert crowd on the subject matter of stigma. We analyze the annotations with visualisation techniques, features from LIWC (Linguistic Inquiry and Word Count) list and make prediction based on bi-grams with traditional and deep learning models. Data augmentation method and application of CNN show high performance accuracy in comparison to other models. Success of the rigorous annotation process on identifying stigma is reconfirmed by achieving high prediction rate with CNN.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.148.pdf

 

An Annotated Dataset of Discourse Modes in Hindi Stories

Swapnil Dhanwal, Hritwik Dutta, Hitesh Nankani, Nilay Shrivastava, Yaman Kumar, Junyi Jessy Li, Debanjan Mahata, Rakesh Gosangi, Haimin Zhang, Rajiv Ratn Shah and Amanda Stent

In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.149.pdf

 

Session Document Classification, Text categorisation

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Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set

Hassan S. Shavarani and Satoshi Sekine

Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions. Structuring Wikipedia is the initial step towards this goal which can facilitate fine-grain classification of articles. In this work, we introduce the Shinra 5-Language Categorization Dataset (SHINRA-5LDS), a large multi-lingual and multi-labeled set of annotated Wikipedia articles in Japanese, English, French, German, and Farsi using Extended Named Entity (ENE) tag set. We evaluate the dataset using the best models provided for ENE label set classification and show that the currently available classification models struggle with large datasets using fine-grained tag sets.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.150.pdf

 

An Algerian Corpus and an Annotation Platform for Opinion and Emotion Analysis

Leila Moudjari, Karima Akli-Astouati and Farah Benamara

In this paper, we address the lack of resources for opinion and emotion analysis related to North African dialects, targeting Algerian dialect. We present TWIFIL (TWItter  proFILing) a collaborative annotation  platform for crowdsourcing annotation of tweets at different levels of granularity. The plateform allowed the creation of the largest Algerian dialect dataset annotated for both sentiment (9,000 tweets), emotion (about 5,000 tweets) and extra-linguistic information including author profiling (age and gender). The annotation resulted also in the creation of the largest Algerien dialect subjectivity  lexicon of about 9,000 entries which can constitute a valuable resources for the development of future NLP applications for Algerian dialect. To test the validity of the dataset, a set of deep learning experiments were conducted to classify a given tweet as positive, negative or neutral. We discuss our results and provide an error analysis to better identify classification errors.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.151.pdf

 

Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task

Valeriya Slovikovskaya and Giuseppe Attardi

Transformer models, trained and publicly released over the last couple of years, have proved effective in many NLP tasks. We wished to test their usefulness in particular on the stance detection task. We performed experiments on the data from the Fake News Challenge Stage 1 (FNC-1). We were indeed able to improve the reported SotA on the challenge, by exploiting the generalization power of large language models based on Transformer architecture. Specifically (1) we improved the FNC-1 best performing model adding BERT sentence embedding of input sequences as a model feature, (2) we fine-tuned BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained state-of-the-art results on FNC-1 task.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.152.pdf

 

Scientific Statement Classification over arXiv.org

Deyan Ginev and Bruce R Miller

We introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of preprint articles. We explore fifty author-annotated categories and empirically motivate a task design of grouping 10.5 million annotated paragraphs into thirteen classes. We demonstrate that the task setup aligns with known success rates from the state of the art, peaking at a 0.91 F1-score via a BiLSTM encoder-decoder model. Additionally, we introduce a lexeme serialization for mathematical formulas, and observe that context-aware models could improve when also trained on the symbolic modality. Finally, we discuss the limitations of both data and task design, and outline potential directions towards increasingly complex models of scientific discourse, beyond isolated statements.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.153.pdf

 

Cross-domain Author Gender Classification in Brazilian Portuguese

Rafael Dias and Ivandré Paraboni

Author profiling models predict demographic characteristics of a target author based on the text that they have written. Systems of this kind will often follow a single-domain approach, in which the model is trained from a corpus of labelled texts in a given domain, and it is subsequently validated against a test corpus built from precisely the same domain. Although single-domain settings are arguably ideal, this strategy gives rise to the question of how to proceed when no suitable training corpus (i.e., a corpus that matches the test domain) is available. To shed light on this issue, this paper discusses a cross-domain gender classification task based on four domains (Facebook, crowd sourced opinions, Blogs and \mbox{E-gov} requests) in the Brazilian Portuguese language. A number of simple gender classification models using word- and psycholinguistics-based features alike are introduced, and their results are compared in two kinds of cross-domain setting: first, by making use of a single text source as training data for each task, and subsequently by combining multiple  sources. Results confirm previous findings related to the effects of corpus size and domain similarity in English, and pave the way for further studies in the field.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.154.pdf

 

 

LEDGAR: A Large-Scale Multi-label Corpus for Text Classification of Legal Provisions in Contracts

Don Tuggener, Pius von Däniken, Thomas Peetz and Mark Cieliebak

We present LEDGAR, a multilabel corpus of legal provisions in contracts. The corpus was crawled and scraped from the public domain (SEC filings) and is, to the best of our knowledge, the first freely available corpus of its kind. Since the corpus was constructed semi-automatically, we apply and discuss various approaches to noise removal. Due to the rather large labelset of over 12'000 labels annotated in almost 100'000 provisions in over 60'000 contracts, we believe the corpus to be of interest for research in the field of Legal NLP, (large-scale or extreme) text classification, as well as for legal studies. We discuss several methods to sample subcopora from the corpus and implement and evaluate different automatic classification approaches. Finally, we perform transfer experiments to evaluate how well the classifiers perform on contracts stemming from outside the corpus.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.155.pdf

 

Online Near-Duplicate Detection of News Articles

Simon Rodier and Dave Carter

Near-duplicate documents are particularly common in news media corpora.  Editors often update wirefeed articles to address space constraints in print editions or to add local context; journalists often lightly modify previous articles with new information or minor corrections. Near-duplicate documents have potentially significant costs, including bloating corpora with redundant information (biasing techniques built upon such corpora) and requiring additional human and computational analytic resources for marginal benefit. Filtering near-duplicates out of a collection is thus important, and is particularly challenging in applications that require them to be filtered out in real-time with high precision. Previous near-duplicate detection methods typically work offline to identify all near-duplicate pairs in a set of documents. We propose an online system which flags a near-duplicate document by finding its most likely original. This system adapts the shingling algorithm proposed by Broder (1997), and we test it on a challenging dataset of web-based news articles. Our online system presents state-of-the-art F1-scores, and can be tuned to trade precision for recall and vice-versa. Given its performance and online nature, our method can be used in many real-world applications. We present one such application, filtering near-duplicates to improve productivity of human analysts in a situational awareness tool.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.156.pdf

 

Automated Essay Scoring System for Nonnative Japanese Learners

Reo Hirao, Mio Arai, Hiroki Shimanaka, Satoru Katsumata and Mamoru Komachi

In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores. In particular, we developed AES systems using two different approaches: a feature-based approach and a neural-network-based approach. In the former approach, we used Japanese-specific linguistic features, including character-type features such as “kanji” and “hiragana.” In the latter approach, we used two models: a long short-term memory (LSTM) model (Hochreiter and Schmidhuber, 1997) and a bidirectional encoder representations from transformers (BERT) model (Devlin et al., 2019), which achieved the highest accuracy in various natural language processing tasks in 2018. Overall, the BERT model achieved the best root mean squared error and quadratic weighted kappa scores. In addition, we analyzed the robustness of the outputs of the BERT model. We have released and shared this system to facilitate further research on AES for Japanese as a second language learners.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.157.pdf

 

A Real-World Data Resource of Complex Sensitive Sentences Based on Documents from the Monsanto Trial

Jan Neerbek, Morten Eskildsen, Peter Dolog and Ira Assent

In this work we present a corpus for the evaluation of sensitive information detection approaches that addresses the need for real world sensitive information for empirical studies. Our sentence corpus contains different notions of complex sensitive information that correspond to different aspects of concern in a current trial of the Monsanto company.

This paper describes the annotations process, where we both employ human annotators and furthermore create automatically inferred labels regarding technical, legal and informal communication within and with employees of Monsanto, drawing on a classification of documents by lawyers involved in the Monsanto court case. We release corpus of high quality sentences and parse trees with these two types of labels on sentence level.

We characterize the sensitive information via several representative sensitive information detection models, in particular both keyword-based (n-gram) approaches and recent deep learning models, namely, recurrent neural networks (LSTM) and recursive neural networks (RecNN).

Data and code are made publicly available.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.158.pdf

 

Discovering Biased News Articles Leveraging Multiple Human Annotations

Konstantina Lazaridou, Alexander Löser, Maria Mestre and Felix Naumann

Unbiased and fair reporting is an integral part of ethical journalism. Yet, political propaganda and one-sided views can be found in the news and can cause distrust in media. Both accidental and deliberate political bias affect the readers and shape their views. We contribute to a trustworthy media ecosystem by automatically identifying politically biased news articles. We introduce novel corpora annotated by two communities, i.e., domain experts and crowd workers, and we also consider automatic article labels inferred by the newspapers’ ideologies. Our goal is to compare domain experts to crowd workers and also to prove that media bias can be detected automatically. We classify news articles with a neural network and we also improve our performance in a self-supervised manner.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.159.pdf

 

Corpora and Baselines for Humour Recognition in Portuguese

Hugo Gonçalo Oliveira, André Clemêncio and Ana Alves

Having in mind the lack of work on the automatic recognition of verbal humour in Portuguese, a topic connected with fluency in a natural language, we describe the creation of three corpora, covering two styles of humour and four sources of non-humorous text, that may be used for related studies. We then report on some experiments where the created corpora were used for training and testing computational models that exploit content and linguistic features for humour recognition. The obtained results helped us taking some conclusions about this challenge and may be seen as baselines for those willing to tackle it in the future, using the same corpora.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.160.pdf

 

FactCorp: A Corpus of Dutch Fact-checks and its Multiple Usages

Marten van der Meulen and W. Gudrun Reijnierse

Fact-checking information before publication has long been a core task for journalists, but recent times have seen the emergence of dedicated news items specifically aimed at fact-checking after publication. This relatively new form of fact-checking receives a fair amount of attention from academics, with current research focusing mostly on journalists’ motivations for publishing post-hoc fact-checks, the effects of fact-checking on the perceived accuracy of false claims, and the creation of computational tools for automatic fact-checking. In this paper, we propose to study fact-checks from a corpus linguistic perspective. This will enable us to gain insight in the scope and contents of fact-checks, to investigate what fact-checks can teach us about the way in which science appears (incorrectly) in the news, and to see how fact-checks behave in the science communication landscape. We report on the creation of FactCorp, a 1,16 million-word corpus containing 1,974 fact-checks from three major Dutch newspapers. We also present results of several exploratory analyses, including a rhetorical moves analysis, a qualitative content elements analysis, and keyword analyses. Through these analyses, we aim to demonstrate the wealth of possible applications that FactCorp allows, thereby stressing the importance of creating such resources.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.161.pdf

 

Automatic Orality Identification in Historical Texts

Katrin Ortmann and Stefanie Dipper

Independently of the medial representation (written/spoken), language can exhibit characteristics of conceptual orality or literacy, which mainly manifest themselves on the lexical or syntactic level. In this paper we aim at automatically identifying conceptually-oral historical texts, with the ultimate goal of gaining knowledge about spoken data of historical time stages. We apply a set of general linguistic features that have been proven to be effective for the classification of modern language data to historical German texts from various registers. Many of the features turn out to be equally useful in determining the conceptuality of historical data as they are for modern data, especially the frequency of different types of pronouns and the ratio of verbs to nouns. Other features like sentence length, particles or interjections point to peculiarities of the historical data and reveal problems with the adoption of a feature set that was developed on modern language data.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.162.pdf

 

Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour

Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta and Angus Roberts

Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs.  We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.163.pdf

 

A First Dataset for Film Age Appropriateness Investigation

Emad Mohamed and Le An Ha

Film age appropriateness classification is an important problem with a significant societal impact that has so far been out of the interest of Natural Language Processing and Machine Learning researchers. To this end, we have collected a corpus of 17000 films along with their age ratings. We use the textual contents in an experiment to predict the correct age classification for the United States (G, PG, PG-13, R and NC-17) and the United Kingdom (U, PG, 12A, 15, 18 and R18).   Our experiments indicate that gradient boosting machines beat FastText and various Deep Learning architectures.  We reach an overall accuracy of 79.3% for the US ratings compared to a projected super human accuracy of 84%. For the UK ratings, we  reach an overall accuracy of 65.3% (UK) compared to a projected super human accuracy of 80.0%.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.164.pdf

 

Habibi - a multi Dialect multi National Arabic Song Lyrics Corpus

Mahmoud El-Haj

This paper introduces Habibi the first Arabic Song Lyrics corpus. The corpus comprises more than 30,000 Arabic song lyrics in 6 Arabic dialects for singers from 18 different Arabic countries. The lyrics are segmented into more than 500,000 sentences (song verses) with more than 3.5 million words. I provide the corpus in both comma separated value (csv) and annotated plain text (txt) file formats. In addition, I converted the csv version into JavaScript Object Notation (json) and eXtensible Markup Language (xml) file formats. To experiment with the corpus I run extensive binary and multi-class experiments for dialect and country-of-origin identification. The identification tasks include the use of several classical machine learning and deep learning models utilising different word embeddings. For the binary dialect identification task the best performing classifier achieved a testing accuracy of 93%. This was achieved using a word-based Convolutional Neural Network (CNN) utilising a Continuous Bag of Words (CBOW) word embeddings model. The results overall show all classical and deep learning models to outperform our baseline, which demonstrates the suitability of the corpus for both dialect and country-of-origin identification tasks. I am making the corpus and the trained CBOW word embeddings freely available for research purposes.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.165.pdf

 

Age Suitability Rating: Predicting the MPAA Rating Based on Movie Dialogues

Mahsa Shafaei, Niloofar Safi Samghabadi, Sudipta Kar and Thamar Solorio

Movies help us learn and inspire societal change. But they can also contain objectionable content that negatively affects viewers' behaviour, especially children. In this paper, our goal is to predict the suitability of movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We create a corpus for movie MPAA ratings and propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 81% weighted F1-score for the classification model that outperforms the traditional machine learning method by 7%.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.166.pdf

 

Email Classification Incorporating Social Networks and Thread Structure

Sakhar Alkhereyf and Owen Rambow

Existing methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the network they form. However, some work has shown that incorporating the social network information in addition to information from language is effective for various NLP applications including sentiment analysis, inferring user attributes, and predicting inter-personal relations. In this paper, we present an empirical study of email classification into ``Business'' and ``Personal'' categories. We represent the email communication using various graph structures. As features, we use both the textual information from the email content and social network information from the communication graphs. We also model the thread structure for emails. We focus on detecting personal emails, and we evaluate our methods on two corpora, only one of which we train on. The experimental results reveal that incorporating social network information improves over the performance of an approach based on textual information only. The results also show that considering the thread structure of emails improves the performance further. Furthermore, our approach improves over a state-of-the-art baseline which uses node embeddings based on both lexical and social network information.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.167.pdf

 

Development and Validation of a Corpus for Machine Humor Comprehension

Yuen-Hsien Tseng, Wun-Syuan Wu, Chia-Yueh Chang, Hsueh-Chih Chen and Wei-Lun Hsu

This work developed a Chinese humor corpus containing 3,365 jokes collected from over 40 sources. Each joke was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. To validate the manual labels, we trained SVM (Support Vector Machine) and BERT (Bidirectional Encoder Representations from Transformers) with half of the corpus (labeled by one annotator) to predict the skill and intent labels of the other half (labeled by the other annotator). Based on two assumptions that a valid manually labeled corpus should follow, our results showed the validity for the skill and intent labels. As to the funniness label, the validation results showed that the correlation between the corpus label and user feedback rating is marginal, which implies that the funniness level is a harder annotation problem to be solved. The contribution of this work is two folds: 1) a Chinese humor corpus is developed with labels of humor skills, intents, and funniness, which allows machines to learn more intricate humor framing, effect, and amusing level to predict and respond in proper context (https://github.com/SamTseng/Chinese_Humor_MultiLabeled). 2) An approach to verify whether a minimum human labeled corpus is valid or not, which facilitates the validation of low-resource corpora.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.168.pdf

 

Alector: A Parallel Corpus of Simplified French Texts with Alignments of Misreadings by Poor and Dyslexic Readers

Núria Gala, Anaïs Tack, Ludivine Javourey-Drevet, Thomas François and Johannes C. Ziegler

In this paper, we present a new parallel corpus addressed to researchers, teachers, and speech therapists interested in text simplification as a means of alleviating difficulties in children learning to read. The corpus is composed of excerpts drawn from 79 authentic literary (tales, stories) and scientific (documentary) texts commonly used in French schools for children aged between 7 to 9 years old. The excerpts were manually simplified at the lexical, morpho-syntactic, and discourse levels in order to propose a parallel corpus for reading tests and for the development of automatic text simplification tools. A sample of 21 poor-reading and dyslexic children with an average reading delay of 2.5 years read a portion of the corpus. The transcripts of readings errors were integrated into the corpus with the goal of identifying lexical difficulty in the target population. By means of statistical testing, we provide evidence that the manual simplifications significantly reduced reading errors, highlighting that the words targeted for simplification were not only well-chosen but also substituted with substantially easier alternatives. The entire corpus is available for consultation through a web interface and available on demand for research purposes.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.169.pdf

 

A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients

Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt and Leo Anthony Celi

A crucial step within secondary analysis of electronic health records (EHRs) is to identify the patient cohort under investigation. While EHRs contain medical billing codes that aim to represent the conditions and treatments patients may have, much of the information is only present in the patient notes. Therefore, it is critical to develop robust algorithms to infer patients' conditions and treatments from their written notes. In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note. Nursing Progress Notes and Discharge Summaries from the Intensive Care Unit of a large tertiary care hospital were manually annotated for the presence of several high-context phenotypes relevant to treatment and risk of re-hospitalization. This dataset contains 1102 Discharge Summaries and 1000 Nursing Progress Notes. Each Discharge Summary and Progress Note has been annotated by at least two expert human annotators (one clinical researcher and one resident physician). Annotated phenotypes include treatment non-adherence, chronic pain, advanced/metastatic cancer, as well as 10 other phenotypes. This dataset can be utilized for academic and industrial research in medicine and computer science, particularly within the field of medical natural language processing.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.170.pdf

 

Multilingual Stance Detection in Tweets: The Catalonia Independence Corpus

Elena Zotova, Rodrigo Agerri, Manuel Nuñez and German Rigau

Stance detection aims to determine the attitude of a given text with respect to a specific topic or claim. While stance detection has been fairly well researched in the last years, most the work has been focused on English. This is mainly due to the relative lack of annotated data in other languages. The TW-10 referendum Dataset released at IberEval 2018 is a previous effort to provide multilingual stance-annotated data in Catalan and Spanish. Unfortunately, the TW-10 Catalan subset is extremely imbalanced. This paper addresses these issues by presenting a new multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages, with the aim of facilitating research on stance detection in multilingual and cross-lingual settings. The dataset is annotated with stance towards one topic, namely, the  ndependence of Catalonia. We also provide a semi-automatic method to annotate the dataset based on a categorization of Twitter users. We experiment on the new corpus with a number of supervised approaches, including linear classifiers and deep learning methods. Comparison of our new corpus with the with the TW-1O dataset shows both the benefits and potential of a well balanced corpus for multilingual and cross-lingual research on stance detection. Finally, we establish new state-of-the-art results on the TW-10 dataset, both for Catalan and Spanish.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.171.pdf

 

An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing

Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth and Georg Groh

A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning. Fine-tuning, the most widely used method for achieving this, suffers from catastrophic forgetting. The problem is often exacerbated in natural language processing (NLP). In this work, we assess progressive neural networks (PNNs) as an alternative to fine-tuning. The evaluation is based on common NLP tasks such as sequence labeling and text classification. By gauging PNNs across a range of architectures, datasets, and tasks, we observe improvements over the baselines throughout all experiments.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.172.pdf

 

WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection

Noé Cécillon, Vincent Labatut, Richard Dufour and Georges Linarès

With the spread of online social networks, it is more and more difficult to monitor all the user-generated content. Automating the moderation process of the inappropriate exchange content on Internet has thus become a priority task. Methods have been proposed for this purpose, but it can be challenging to find a suitable dataset to train and develop them. This issue is especially true for approaches based on information derived from the structure and the dynamic of the conversation. In this work, we propose an original framework, based on the the Wikipedia Comment corpus, with comment-level abuse annotations of different types. The major contribution concerns the reconstruction of conversations, by comparison to existing corpora, which focus only on isolated messages (i.e. taken out of their conversational context). This large corpus of more than 380k annotated messages opens perspectives for online abuse detection and especially for context-based approaches. We also propose, in addition to this corpus, a complete benchmarking platform to stimulate and fairly compare scientific works around the problem of content abuse detection, trying to avoid the recurring problem of result replication. Finally, we apply two classification methods to our dataset to demonstrate its potential.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.173.pdf

 

FloDusTA: Saudi Tweets Dataset for Flood, Dust Storm, and Traffic Accident Events

Btool Hamoui, Mourad Mars and Khaled Almotairi

The rise of social media platforms makes it a valuable information source of recent events and users’ perspective towards them. Twitter has been one of the most important communication platforms in recent years. Event detection, one of the information extraction aspects, involves identifying specified types of events in the text. Detecting events from tweets can help to predict real-world events precisely. A serious challenge that faces Arabic event detection is the lack of Arabic datasets that can be exploited in detecting events. This paper will describe FloDusTA, which is a dataset of tweets that we have built for the purpose of developing an event detection system. The dataset contains tweets written in both Modern Standard Arabic and Saudi dialect. The process of building the dataset starting from tweets collection to annotation by human annotators will be present. The tweets are labeled with four labels: flood, dust storm, traffic accident, and non-event. The dataset was tested for classification and the result was strongly encouraging.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.174.pdf

 

An Annotated Corpus for Sexism Detection in French Tweets

Patricia Chiril, Véronique Moriceau, Farah Benamara, Alda Mari, Gloria Origgi and Marlène Coulomb-Gully

Social media networks have become a space where users are free to relate their opinions and sentiments which may lead to a large spreading of hatred or abusive messages which have to be moderated. This paper presents the first French corpus annotated for sexism detection composed of about 12,000 tweets. In a context of offensive content mediation on social media now regulated by European laws, we think that it is important to be able to detect automatically not only sexist content but also to identify if a message with a sexist content is really sexist (i.e. addressed to a woman or describing a woman or women in general) or is a story of sexism experienced by a woman. This point is the novelty of our annotation scheme. We also propose some preliminary results for sexism detection obtained with a deep learning approach. Our experiments show encouraging results.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.175.pdf

 

Measuring the Impact of Readability Features in Fake News Detection

Roney Santos, Gabriela Pedro, Sidney Leal, Oto Vale, Thiago Pardo, Kalina Bontcheva and Carolina Scarton

The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, recent initiatives tried to detect fake news in different ways, ranging from language-based approaches to content-based verification. In such approaches, the choice of the features for the classification of fake and true news is one of the most important parts of the process. This paper presents a study on the impact of readability features to detect fake news for the Brazilian Portuguese language. The results show that such features are relevant to the task (achieving, alone, up to 92% classification accuracy) and may improve previous classification results.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.176.pdf

 

When Shallow is Good Enough: Automatic Assessment of Conceptual Text Complexity using Shallow Semantic Features

Sanja Stajner and Ioana Hulpuș

According to psycholinguistic studies, the complexity of concepts used in a text and the relations between mentioned concepts play the most important role in text understanding and maintaining reader’s interest. However, the classical approaches to automatic assessment of text complexity, and their commercial applications, take into consideration mainly syntactic and lexical complexity. Recently, we introduced the task of automatic assessment of conceptual text complexity, proposing a set of graph-based deep semantic features using DBpedia as a proxy to human knowledge. Given that such graphs can be noisy, incomplete, and computationally expensive to deal with, in this paper, we propose the use of textual features and shallow semantic features that only require entity linking. We compare the results obtained with new features with those of the state-of-the-art deep semantic features on two tasks: (1) pairwise comparison of two versions of the same text; and (2) five-level classification of texts. We find that the shallow features achieve state-of-the-art results on both tasks, significantly outperforming performances of the deep semantic features on the five-level classification task. Interestingly, the combination of the shallow and deep semantic features lead to a significant improvement of the performances on that task.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.177.pdf

 

DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text

Pasquale Capuozzo, Ivano Lauriola, Carlo Strapparava, Fabio Aiolli and Giuseppe Sartori

In recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these issues is challenging since labelled corpora involving multiple domains and compiled in more than one language are few in the scientific literature. For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios. DecOp is composed of 5000 examples of both truthful and deceitful first-person opinions balanced both across five different domains and two languages and, to the best of our knowledge, is the largest corpus allowing cross-domain and cross-language comparisons in deceit detection tasks. In this paper, we describe the collection procedure of the DecOp corpus and his main characteristics. Moreover, the human performance on the DecOp test-set and preliminary experiments by means of machine learning models based on Transformer architecture are shown.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.178.pdf

 

Age Recommendation for Texts

Alexis Blandin, Gwénolé Lecorvé, Delphine Battistelli and Aline Étienne

The understanding of a text by a reader or listener is conditioned by the adequacy of the text's characteristics with the person's capacities and knowledge. This adequacy is critical in the case of a child since her/his cognitive and linguistic skills are still under development. Hence, in this paper, we present and study an original natural language processing (NLP) task which consists in predicting the age from which a text can be understood by someone. To do so, this paper first exhibits features derived from the psycholinguistic domain, as well as some coming from related NLP tasks. Then, we propose a set of neural network models and compare them on a dataset of French texts dedicated to young or adult audiences. To circumvent the lack of data, we study the idea to predict ages at the sentence level. The experiments first show that the sentence-based age recommendations can be efficiently merged to predict text-based recommendations. Then, we also demonstrate that the age predictions returned by our best model are better than those provided by psycholinguists. Finally, the paper investigates the impact of the various features used in these results.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.179.pdf

 

Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition

Xiaolei Huang, Linzi Xing, Franck Dernoncourt and Michael J. Paul

Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes. In this work, we assemble and publish a multilingual Twitter corpus for the task of hate speech detection with inferred four author demographic factors: age, country, gender and race/ethnicity. The corpus covers five languages: English, Italian, Polish, Portuguese and Spanish. We evaluate the inferred demographic labels with a crowdsourcing platform, Figure Eight. To examine factors that can cause biases, we take an empirical analysis of demographic predictability on the English corpus. We measure the performance of four popular document classifiers and evaluate the fairness and bias of the baseline classifiers on the author-level demographic attributes.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.180.pdf

 

VICTOR: a Dataset for Brazilian Legal Documents Classification

Pedro Henrique Luz de Araujo, Teófilo Emídio de Campos, Fabricio Ataides Braz and Nilton Correia da Silva

This paper describes VICTOR, a novel dataset built from Brazil's Supreme Court digitalized legal documents, composed of more than 45 thousand appeals, which includes roughly 692 thousand documents---about 4.6 million pages. The dataset contains labeled text data and supports two types of tasks: document type classification; and theme assignment, a multilabel problem. We present baseline results using bag-of-words models, convolutional neural networks, recurrent neural networks and boosting algorithms. We also experiment using linear-chain Conditional Random Fields to leverage the sequential nature of the lawsuits, which we find to lead to improvements on document type classification. Finally we compare a theme classification approach where we use domain knowledge to filter out the less informative document pages to the default one where we use all pages. Contrary to the Court experts' expectations, we find that using all available data is the better method.  We make the dataset available in three versions of different sizes and contents to encourage explorations of better models and techniques.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.181.pdf

 

Dynamic Classification in Web Archiving Collections

Krutarth Patel, Cornelia Caragea and Mark Phillips

The Web archived data usually contains high-quality documents that are very useful for creating specialized collections of documents. To create such collections, there is a substantial need for automatic approaches that can distinguish the documents of interest for a collection out of the large collections (of millions in size) from Web Archiving institutions. However, the patterns of the documents of interest can differ substantially from one document to another, which makes the automatic classification task very challenging. In this paper, we explore dynamic fusion models to find, on the fly, the model or combination of models that performs best on a variety of document types. Our experimental results show that the approach that fuses different models outperforms individual models and other ensemble methods on three datasets.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.182.pdf

 

Aspect Flow Representation and Audio Inspired Analysis for Texts

Larissa Vasconcelos, Claudio Campelo and Caio Jeronimo

For better understanding how people write texts, it is fundamental to examine how a particular aspect (e.g., subjectivity, sentiment, argumentation) is exploited in a text. Analysing such an aspect of a text as a whole (i.e., through a summarised single feature) can lead to significant information loss. In this paper, we propose a novel method of representing and analysing texts that consider how an aspect behaves throughout the text. We represent the texts by aspect flows for capturing all the aspect behaviour. Then, inspired by the resemblance between these flows format and a sound waveform, we fragment them into frames and calculate an adaptation of audio analysis features, named here Audio-Like Features, as a way of analysing the texts. The results of the conducted classification tasks reveal that our approach can surpass methods based on summarised features. We also show that a detailed examination of the Audio-Like Features can lead to a more profound knowledge about the represented texts.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.183.pdf

 

Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing

Sora Lim, Adam Jatowt, Michael Färber and Masatoshi Yoshikawa

The spread of biased news and its consumption by the readers has become a considerable issue. Researchers from multiple domains including social science and media studies have made efforts to mitigate this media bias issue. Specifically, various techniques ranging from natural language processing to machine learning have been used to help determine news bias automatically. However, due to the lack of publicly available datasets in this field, especially ones containing labels concerning bias on a fine-grained level (e.g., on sentence level), it is still challenging to develop methods for effectively identifying bias embedded in new articles. In this paper, we propose a novel news bias dataset which facilitates the development and evaluation of approaches for detecting subtle bias in news articles and for understanding the characteristics of biased sentences. Our dataset consists of 966 sentences from 46 English-language news articles covering 4 different events and contains labels concerning bias on the sentence level. For scalability reasons, the labels were obtained based on crowd-sourcing. Our dataset can be used for analyzing news bias, as well as for developing and evaluating methods for news bias detection. It can also serve as resource for related researches including ones focusing on fake news detection.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.184.pdf

 

Evaluation of Deep Gaussian Processes for Text Classification

P. Jayashree and P. K. Srijith

With the tremendous success of deep learning models on computer vision tasks, there are various emerging works on the Natural Language Processing (NLP) task of Text Classification using parametric models. However, it constrains the expressability limit of the function and demands enormous empirical efforts to come up with a robust model architecture. Also, the huge parameters involved in the model causes over-fitting when dealing with small datasets. Deep Gaussian Processes (DGP) offer a Bayesian non-parametric modelling framework with strong function compositionality, and helps in overcoming these limitations. In this paper, we propose DGP models for the task of Text Classification and an empirical comparison of the performance of shallow and Deep Gaussian Process models is made. Extensive experimentation is performed on the benchmark Text Classification datasets such as TREC (Text REtrieval Conference), SST (Stanford Sentiment Treebank), MR (Movie Reviews), R8 (Reuters-8), which demonstrate the effectiveness of DGP models.

http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.185.pdf

 

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