pwc-archive/papers-with-abstracts huggingface dataset:
Learning from Within? Comparing PoS Tagging Approaches for Historical Text
Authors: Sarah Schulz, Jonas Kuhn
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Part-Of-Speech Tagging, POS, POS Tagging, Self-Learning

In this paper, we investigate unsupervised and semi-supervised methods for part-of-speech (PoS) tagging in the context of historical German text. We locate our research in the context of Digital Humanities where the non-canonical nature of text causes issues facing an Natural Language Processing world in which tools are mainly trained on standard data. Data deviating from the norm requires tools adjusted to this data. We explore to which extend the availability of such training material and resources related to it influences the accuracy of PoS tagging. We investigate a variety of algorithms including neural nets, conditional random fields and self-learning techniques in order to find the best-fitted approach to tackle data sparsity. Although methods using resources from related languages outperform weakly supervised methods using just a few training examples, we can still reach a promising accuracy with methods abstaining additional resources.
Wow! What a Useful Extension! Introducing Non-Referential Concepts to Wordnet
Authors: Luis Morgado Da Costa, Francis Bond
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper we present the ongoing efforts to expand the depth and breath of the Open Multilingual Wordnet coverage by introducing two new classes of non-referential concepts to wordnet hierarchies: interjections and numeral classifiers. The lexical semantic hierarchy pioneered by Princeton Wordnet has traditionally restricted its coverage to referential and contentful classes of words: such as nouns, verbs, adjectives and adverbs. Previous efforts have been employed to enrich wordnet resources including, for example, the inclusion of pronouns, determiners and quantifiers within their hierarchies. Following similar efforts, and motivated by the ongoing semantic annotation of the NTU-Multilingual Corpus, we decided that the four traditional classes of words present in wordnets were too restrictive. Though non-referential, interjections and classifiers possess interesting semantics features that can be well captured by lexical resources like wordnets. In this paper, we will further motivate our decision to include non-referential concepts in wordnets and give an account of the current state of this expansion.
SlangNet: A WordNet like resource for English Slang
Authors: Shehzaad Dhuliawala, Diptesh Kanojia, Pushpak Bhattacharyya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Information Retrieval, Retrieval, Word Sense Disambiguation

We present a WordNet like structured resource for slang words and neologisms on the internet. The dynamism of language is often an indication that current language technology tools trained on today{'}s data, may not be able to process the language in the future. Our resource could be (1) used to augment the WordNet, (2) used in several Natural Language Processing (NLP) applications which make use of noisy data on the internet like Information Retrieval and Web Mining. Such a resource can also be used to distinguish slang word senses from conventional word senses. To stimulate similar innovations widely in the NLP community, we test the efficacy of our resource for detecting slang using standard bag of words Word Sense Disambiguation (WSD) algorithms (Lesk and Extended Lesk) for English data on the internet.
Discovering Fuzzy Synsets from the Redundancy in Different Lexical-Semantic Resources
Authors: Hugo Gon{\c{c}}alo Oliveira, F{\'a}bio Santos
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Although represented as such in wordnets, word senses are not discrete. To handle word senses as fuzzy objects, we exploit the graph structure of synonymy pairs acquired from different sources to discover synsets where words have different membership degrees that reflect confidence. Following this approach, a wide-coverage fuzzy thesaurus was discovered from a synonymy network compiled from seven Portuguese lexical-semantic resources. Based on a crowdsourcing evaluation, we can say that the quality of the obtained synsets is far from perfect but, as expected in a confidence measure, it increases significantly for higher cut-points on the membership and, at a certain point, reaches 100{\%} correction rate.
The Hebrew FrameNet Project
Authors: Avi Hayoun, Michael Elhadad
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We present the Hebrew FrameNet project, describe the development and annotation processes and enumerate the challenges we faced along the way. We have developed semi-automatic tools to help speed the annotation and data collection process. The resource currently covers 167 frames, 3,000 lexical units and about 500 fully annotated sentences. We have started training and testing automatic SRL tools on the seed data.
An Open Corpus for Named Entity Recognition in Historic Newspapers
Authors: Clemens Neudecker
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The availability of openly available textual datasets ({``}corpora{''}) with highly accurate manual annotations ({``}gold standard{''}) of named entities (e.g. persons, locations, organizations, etc.) is crucial in the training and evaluation of named entity recognition systems. Currently there are only few such datasets available on the web, and even less for texts containing historical spelling variation. The production and subsequent release into the public domain of four such datasets with 100 pages each for the languages Dutch, French, German (including Austrian) as part of the Europeana Newspapers project is expected to contribute to the further development and improvement of named entity recognition systems with a focus on historical content. This paper describes how these datasets were produced, what challenges were encountered in their creation and informs about their final quality and availability.
Ambiguity Diagnosis for Terms in Digital Humanities
Authors: B{\'e}atrice Daille, Evelyne Jacquey, Ga{\"e}l Lejeune, Luis Felipe Melo, Yannick Toussaint
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: All, Word Sense Disambiguation

Among all researches dedicating to terminology and word sense disambiguation, little attention has been devoted to the ambiguity of term occurrences. If a lexical unit is indeed a term of the domain, it is not true, even in a specialised corpus, that all its occurrences are terminological. Some occurrences are terminological and other are not. Thus, a global decision at the corpus level about the terminological status of all occurrences of a lexical unit would then be erroneous. In this paper, we propose three original methods to characterise the ambiguity of term occurrences in the domain of social sciences for French. These methods differently model the context of the term occurrences: one is relying on text mining, the second is based on textometry, and the last one focuses on text genre properties. The experimental results show the potential of the proposed approaches and give an opportunity to discuss about their hybridisation.
Metrical Annotation of a Large Corpus of Spanish Sonnets: Representation, Scansion and Evaluation
Authors: Borja Navarro, Mar{\'\i}a Ribes Lafoz, Noelia S{\'a}nchez
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In order to analyze metrical and semantics aspects of poetry in Spanish with computational techniques, we have developed a large corpus annotated with metrical information. In this paper we will present and discuss the development of this corpus: the formal representation of metrical patterns, the semi-automatic annotation process based on a new automatic scansion system, the main annotation problems, and the evaluation, in which an inter-annotator agreement of 96{\%} has been obtained. The corpus is open and available.
Corpus Analysis based on Structural Phenomena in Texts: Exploiting TEI Encoding for Linguistic Research
Authors: Susanne Haaf
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper poses the question, how linguistic corpus-based research may be enriched by the exploitation of conceptual text structures and layout as provided via TEI annotation. Examples for possible areas of research and usage scenarios are provided based on the German historical corpus of the Deutsches Textarchiv (DTA) project, which has been consistently tagged accordant to the TEI Guidelines, more specifically to the DTA ›Base Format‹ (DTABf). The paper shows that by including TEI-XML structuring in corpus-based analyses significances can be observed for different linguistic phenomena, as e.g. the development of conceptual text structures themselves, the syntactic embedding of terms in certain conceptual text structures, and phenomena of language change which become obvious via the layout of a text. The exemplary study carried out here shows some of the potential for the exploitation of TEI annotation for linguistic research, which might be kept in mind when making design decisions for new corpora as well when working with existing TEI corpora.
Evaluating Entity Linking: An Analysis of Current Benchmark Datasets and a Roadmap for Doing a Better Job
Authors: Marieke van Erp, Pablo Mendes, Heiko Paulheim, Filip Ilievski, Julien Plu, Giuseppe Rizzo, Joerg Waitelonis
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Entity Linking

Entity linking has become a popular task in both natural language processing and semantic web communities. However, we find that the benchmark datasets for entity linking tasks do not accurately evaluate entity linking systems. In this paper, we aim to chart the strengths and weaknesses of current benchmark datasets and sketch a roadmap for the community to devise better benchmark datasets.
Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection
Authors: Daniel Preo{\c{t}}iuc-Pietro, P. K. Srijith, Mark Hepple, Trevor Cohn
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, Event Detection

Streaming media provides a number of unique challenges for computational linguistics. This paper studies the temporal variation in word co-occurrence statistics, with application to event detection. We develop a spectral clustering approach to find groups of mutually informative terms occurring in discrete time frames. Experiments on large datasets of tweets show that these groups identify key real world events as they occur in time, despite no explicit supervision. The performance of our method rivals state-of-the-art methods for event detection on F-score, obtaining higher recall at the expense of precision.
Markov Logic Networks for Text Mining: A Qualitative and Empirical Comparison with Integer Linear Programming
Authors: Luis Gerardo Mojica de la Vega, Vincent Ng
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Joint inference approaches such as Integer Linear Programming (ILP) and Markov Logic Networks (MLNs) have recently been successfully applied to many natural language processing (NLP) tasks, often outperforming their pipeline counterparts. However, MLNs are arguably much less popular among NLP researchers than ILP. While NLP researchers who desire to employ these joint inference frameworks do not necessarily have to understand their theoretical underpinnings, it is imperative that they understand which of them should be applied under what circumstances. With the goal of helping NLP researchers better understand the relative strengths and weaknesses of MLNs and ILP; we will compare them along different dimensions of interest, such as expressiveness, ease of use, scalability, and performance. To our knowledge, this is the first systematic comparison of ILP and MLNs on an NLP task.
Arabic Corpora for Credibility Analysis
Authors: Ayman Al Zaatari, Rim El Ballouli, Shady ELbassouni, Wassim El-Hajj, Hazem Hajj, Khaled Shaban, Nizar Habash, Emad Yahya
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: BIG-bench Machine Learning, General Classification

A significant portion of data generated on blogging and microblogging websites is non-credible as shown in many recent studies. To filter out such non-credible information, machine learning can be deployed to build automatic credibility classifiers. However, as in the case with most supervised machine learning approaches, a sufficiently large and accurate training data must be available. In this paper, we focus on building a public Arabic corpus of blogs and microblogs that can be used for credibility classification. We focus on Arabic due to the recent popularity of blogs and microblogs in the Arab World and due to the lack of any such public corpora in Arabic. We discuss our data acquisition approach and annotation process, provide rigid analysis on the annotated data and finally report some results on the effectiveness of our data for credibility classification.
Solving the AL Chicken-and-Egg Corpus and Model Problem: Model-free Active Learning for Phenomena-driven Corpus Construction
Authors: Dain Kaplan, Neil Rubens, Simone Teufel, Takenobu Tokunaga
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Active Learning, model

Active learning (AL) is often used in corpus construction (CC) for selecting {``}informative{''} documents for annotation. This is ideal for focusing annotation efforts when all documents cannot be annotated, but has the limitation that it is carried out in a closed-loop, selecting points that will improve an existing model. For phenomena-driven and exploratory CC, the lack of existing-models and specific task(s) for using it make traditional AL inapplicable. In this paper we propose a novel method for model-free AL utilising characteristics of phenomena for applying AL to select documents for annotation. The method can also supplement traditional closed-loop AL-based CC to extend the utility of the corpus created beyond a single task. We introduce our tool, MOVE, and show its potential with a real world case-study.
QUEMDISSE? Reported speech in Portuguese
Authors: Cl{\'a}udia Freitas, Bianca Freitas, Diana Santos
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents some work on direct and indirect speech in Portuguese using corpus-based methods: we report on a study whose aim was to identify (i) Portuguese verbs used to introduce reported speech and (ii) syntactic patterns used to convey reported speech, in order to enhance the performance of a quotation extraction system, dubbed QUEMDISSE?. In addition, (iii) we present a Portuguese corpus annotated with reported speech, using the lexicon and rules provided by (i) and (ii), and discuss the process of their annotation and what was learned.
MEANTIME, the NewsReader Multilingual Event and Time Corpus
Authors: Anne-Lyse Minard, Manuela Speranza, Ruben Urizar, Bego{\~n}a Altuna, Marieke van Erp, Anneleen Schoen, Chantal van Son
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Semantic Role Labeling

In this paper, we present the NewsReader MEANTIME corpus, a semantically annotated corpus of Wikinews articles. The corpus consists of 480 news articles, i.e. 120 English news articles and their translations in Spanish, Italian, and Dutch. MEANTIME contains annotations at different levels. The document-level annotation includes markables (e.g. entity mentions, event mentions, time expressions, and numerical expressions), relations between markables (modeling, for example, temporal information and semantic role labeling), and entity and event intra-document coreference. The corpus-level annotation includes entity and event cross-document coreference. Semantic annotation on the English section was performed manually; for the annotation in Italian, Spanish, and (partially) Dutch, a procedure was devised to automatically project the annotations on the English texts onto the translated texts, based on the manual alignment of the annotated elements; this enabled us not only to speed up the annotation process but also provided cross-lingual coreference. The English section of the corpus was extended with timeline annotations for the SemEval 2015 TimeLine shared task. The {``}First CLIN Dutch Shared Task{''} at CLIN26 was based on the Dutch section, while the EVALITA 2016 FactA (Event Factuality Annotation) shared task, based on the Italian section, is currently being organized.
The ACQDIV Database: Min(d)ing the Ambient Language
Authors: Steven Moran
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Diversity, Language Acquisition

One of the most pressing questions in cognitive science remains unanswered: what cognitive mechanisms enable children to learn any of the world{'}s 7000 or so languages? Much discovery has been made with regard to specific learning mechanisms in specific languages, however, given the remarkable diversity of language structures (Evans and Levinson 2009, Bickel 2014) the burning question remains: what are the underlying processes that make language acquisition possible, despite substantial cross-linguistic variation in phonology, morphology, syntax, etc.? To investigate these questions, a comprehensive cross-linguistic database of longitudinal child language acquisition corpora from maximally diverse languages has been built.
Summarizing Behaviours: An Experiment on the Annotation of Call-Centre Conversations
Authors: Morena Danieli, Balamurali A.R, Evgeny Stepanov, Benoit Favre, Frederic Bechet, Giuseppe Riccardi
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Annotating and predicting behavioural aspects in conversations is becoming critical in the conversational analytics industry. In this paper we look into inter-annotator agreement of agent behaviour dimensions on two call center corpora. We find that the task can be annotated consistently over time, but that subjectivity issues impacts the quality of the annotation. The reformulation of some of the annotated dimensions is suggested in order to improve agreement.
Survey of Conversational Behavior: Towards the Design of a Balanced Corpus of Everyday Japanese Conversation
Authors: Hanae Koiso, Tomoyuki Tsuchiya, Ryoko Watanabe, Daisuke Yokomori, Masao Aizawa, Yasuharu Den
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Survey

In 2016, we set about building a large-scale corpus of everyday Japanese conversationâ€•a collection of conversations embedded in naturally occurring activities in daily life. We will collect more than 200 hours of recordings over six years,publishing the corpus in 2022. To construct such a huge corpus, we have conducted a pilot project, one of whose purposes is to establish a corpus design for collecting various kinds of everyday conversations in a balanced manner. For this purpose, we conducted a survey of everyday conversational behavior, with about 250 adults, in order to reveal how diverse our everyday conversational behavior is and to build an empirical foundation for corpus design. The questionnaire included when, where, how long,with whom, and in what kind of activity informants were engaged in conversations. We found that ordinary conversations show the following tendencies: i) they mainly consist of chats, business talks, and consultations; ii) in general, the number of participants is small and the duration of the conversation is short; iii) many conversations are conducted in private places such as homes, as well as in public places such as offices and schools; and iv) some questionnaire items are related to each other. This paper describes an overview of this survey study, and then discusses how to design a large-scale corpus of everyday Japanese conversation on this basis.
A Multi-party Multi-modal Dataset for Focus of Visual Attention in Human-human and Human-robot Interaction
Authors: Kalin Stefanov, Jonas Beskow
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This papers describes a data collection setup and a newly recorded dataset. The main purpose of this dataset is to explore patterns in the focus of visual attention of humans under three different conditions - two humans involved in task-based interaction with a robot; same two humans involved in task-based interaction where the robot is replaced by a third human, and a free three-party human interaction. The dataset contains two parts - 6 sessions with duration of approximately 3 hours and 9 sessions with duration of approximately 4.5 hours. Both parts of the dataset are rich in modalities and recorded data streams - they include the streams of three Kinect v2 devices (color, depth, infrared, body and face data), three high quality audio streams, three high resolution GoPro video streams, touch data for the task-based interactions and the system state of the robot. In addition, the second part of the dataset introduces the data streams from three Tobii Pro Glasses 2 eye trackers. The language of all interactions is English and all data streams are spatially and temporally aligned.
Capturing Chat: Annotation and Tools for Multiparty Casual Conversation.
Authors: Emer Gilmartin, Nick Campbell
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

Casual multiparty conversation is an understudied but very common genre of spoken interaction, whose analysis presents a number of challenges in terms of data scarcity and annotation. We describe the annotation process used on the d64 and DANS multimodal corpora of multiparty casual talk, which have been manually segmented, transcribed, annotated for laughter and disfluencies, and aligned using the Penn Aligner. We also describe a visualization tool, STAVE, developed during the annotation process, which allows long stretches of talk or indeed entire conversations to be viewed, aiding preliminary identification of features and patterns worthy of analysis. It is hoped that this tool will be of use to other researchers working in this field.
Privacy Issues in Online Machine Translation Services - European Perspective
Authors: Pawel Kamocki, Jim O{'}Regan
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

In order to develop its full potential, global communication needs linguistic support systems such as Machine Translation (MT). In the past decade, free online MT tools have become available to the general public, and the quality of their output is increasing. However, the use of such tools may entail various legal implications, especially as far as processing of personal data is concerned. This is even more evident if we take into account that their business model is largely based on providing translation in exchange for data, which can subsequently be used to improve the translation model, but also for commercial purposes. The purpose of this paper is to examine how free online MT tools fit in the European data protection framework, harmonised by the EU Data Protection Directive. The perspectives of both the user and the MT service provider are taken into account.
Lin|gu|is|tik: Building the Linguist's Pathway to Bibliographies, Libraries, Language Resources and Linked Open Data
Authors: Christian Chiarcos, Christian F{\"a}th, Heike Renner-Westermann, Frank Abromeit, Vanya Dimitrova
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper introduces a novel research tool for the field of linguistics: The Lin|gu|is|tik web portal provides a virtual library which offers scientific information on every linguistic subject. It comprises selected internet sources and databases as well as catalogues for linguistic literature, and addresses an interdisciplinary audience. The virtual library is the most recent outcome of the Special Subject Collection Linguistics of the German Research Foundation (DFG), and also integrates the knowledge accumulated in the Bibliography of Linguistic Literature. In addition to the portal, we describe long-term goals and prospects with a special focus on ongoing efforts regarding an extension towards integrating language resources and Linguistic Linked Open Data.