pwc-archive/papers-with-abstracts huggingface dataset:
The Language Application Grid and Galaxy
Authors: Nancy Ide, Keith Suderman, James Pustejovsky, Marc Verhagen, Christopher Cieri
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management

The NSF-SI2-funded LAPPS Grid project is a collaborative effort among Brandeis University, Vassar College, Carnegie-Mellon University (CMU), and the Linguistic Data Consortium (LDC), which has developed an open, web-based infrastructure through which resources can be easily accessed and within which tailored language services can be efficiently composed, evaluated, disseminated and consumed by researchers, developers, and students across a wide variety of disciplines. The LAPPS Grid project recently adopted Galaxy (Giardine et al., 2005), a robust, well-developed, and well-supported front end for workflow configuration, management, and persistence. Galaxy allows data inputs and processing steps to be selected from graphical menus, and results are displayed in intuitive plots and summaries that encourage interactive workflows and the exploration of hypotheses. The Galaxy workflow engine provides significant advantages for deploying pipelines of LAPPS Grid web services, including not only means to create and deploy locally-run and even customized versions of the LAPPS Grid as well as running the LAPPS Grid in the cloud, but also access to a huge array of statistical and visualization tools that have been developed for use in genomics research.
ELRA Activities and Services
Authors: Khalid Choukri, Val{\'e}rie Mapelli, H{\'e}l{\`e}ne Mazo, Vladimir Popescu
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Management

After celebrating its 20th anniversary in 2015, ELRA is carrying on its strong involvement in the HLT field. To share ELRA{'}s expertise of those 21 past years, this article begins with a presentation of ELRA{'}s strategic Data and LR Management Plan for a wide use by the language communities. Then, we further report on ELRA{'}s activities and services provided since LREC 2014. When looking at the cataloguing and licensing activities, we can see that ELRA has been active at making the Meta-Share repository move toward new developments steps, supporting Europe to obtain accurate LRs within the Connecting Europe Facility programme, promoting the use of LR citation, creating the ELRA License Wizard web portal.The article further elaborates on the recent LR production activities of various written, speech and video resources, commissioned by public and private customers. In parallel, ELDA has also worked on several EU-funded projects centred on strategic issues related to the European Digital Single Market. The last part gives an overview of the latest dissemination activities, with a special focus on the celebration of its 20th anniversary organised in Dubrovnik (Croatia) and the following up of LREC, as well as the launching of the new ELRA portal.
Mirroring Facial Expressions and Emotions in Dyadic Conversations
Authors: Costanza Navarretta
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper presents an investigation of mirroring facial expressions and the emotions which they convey in dyadic naturally occurring first encounters. Mirroring facial expressions are a common phenomenon in face-to-face interactions, and they are due to the mirror neuron system which has been found in both animals and humans. Researchers have proposed that the mirror neuron system is an important component behind many cognitive processes such as action learning and understanding the emotions of others. Preceding studies of the first encounters have shown that overlapping speech and overlapping facial expressions are very frequent. In this study, we want to determine whether the overlapping facial expressions are mirrored or are otherwise correlated in the encounters, and to what extent mirroring facial expressions convey the same emotion. The results of our study show that the majority of smiles and laughs, and one fifth of the occurrences of raised eyebrows are mirrored in the data. Moreover some facial traits in co-occurring expressions co-occur more often than it would be expected by chance. Finally, amusement, and to a lesser extent friendliness, are often emotions shared by both participants, while other emotions indicating individual affective states such as uncertainty and hesitancy are never showed by both participants, but co-occur with complementary emotions such as friendliness and support. Whether these tendencies are specific to this type of conversations or are more common should be investigated further.
A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults
Authors: Victoria Yaneva, Irina Temnikova, Ruslan Mitkov
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Multiple-choice, POS, Reading Comprehension, Text Simplification

The paper presents a corpus of text data and its corresponding gaze fixations obtained from autistic and non-autistic readers. The data was elicited through reading comprehension testing combined with eye-tracking recording. The corpus consists of 1034 content words tagged with their POS, syntactic role and three gaze-based measures corresponding to the autistic and control participants. The reading skills of the participants were measured through multiple-choice questions and, based on the answers given, they were divided into groups of skillful and less-skillful readers. This division of the groups informs researchers on whether particular fixations were elicited from skillful or less-skillful readers and allows a fair between-group comparison for two levels of reading ability. In addition to describing the process of data collection and corpus development, we present a study on the effect that word length has on reading in autism. The corpus is intended as a resource for investigating the particular linguistic constructions which pose reading difficulties for people with autism and hopefully, as a way to inform future text simplification research intended for this population.
A Multimodal Corpus for the Assessment of Public Speaking Ability and Anxiety
Authors: Mathieu Chollet, Torsten W{\"o}rtwein, Louis-Philippe Morency, Stefan Scherer
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: multimodal interaction

The ability to efficiently speak in public is an essential asset for many professions and is used in everyday life. As such, tools enabling the improvement of public speaking performance and the assessment and mitigation of anxiety related to public speaking would be very useful. Multimodal interaction technologies, such as computer vision and embodied conversational agents, have recently been investigated for the training and assessment of interpersonal skills. Once central requirement for these technologies is multimodal corpora for training machine learning models. This paper addresses the need of these technologies by presenting and sharing a multimodal corpus of public speaking presentations. These presentations were collected in an experimental study investigating the potential of interactive virtual audiences for public speaking training. This corpus includes audio-visual data and automatically extracted features, measures of public speaking anxiety and personality, annotations of participants{'} behaviors and expert ratings of behavioral aspects and overall performance of the presenters. We hope this corpus will help other research teams in developing tools for supporting public speaking training.
Deep Learning of Audio and Language Features for Humor Prediction
Authors: Dario Bertero, Pascale Fung
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: BIG-bench Machine Learning, Deep Learning

We propose a comparison between various supervised machine learning methods to predict and detect humor in dialogues. We retrieve our humorous dialogues from a very popular TV sitcom: {``}The Big Bang Theory{''}. We build a corpus where punchlines are annotated using the canned laughter embedded in the audio track. Our comparative study involves a linear-chain Conditional Random Field over a Recurrent Neural Network and a Convolutional Neural Network. Using a combination of word-level and audio frame-level features, the CNN outperforms the other methods, obtaining the best F-score of 68.5{\%} over 66.5{\%} by CRF and 52.9{\%} by RNN. Our work is a starting point to developing more effective machine learning and neural network models on the humor prediction task, as well as developing machines capable in understanding humor in general.
An Empirical Study of Arabic Formulaic Sequence Extraction Methods
Authors: Ayman Alghamdi, Eric Atwell, Claire Brierley
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper aims to implement what is referred to as the collocation of the Arabic keywords approach for extracting formulaic sequences (FSs) in the form of high frequency but semantically regular formulas that are not restricted to any syntactic construction or semantic domain. The study applies several distributional semantic models in order to automatically extract relevant FSs related to Arabic keywords. The data sets used in this experiment are rendered from a new developed corpus-based Arabic wordlist consisting of 5,189 lexical items which represent a variety of modern standard Arabic (MSA) genres and regions, the new wordlist being based on an overlapping frequency based on a comprehensive comparison of four large Arabic corpora with a total size of over 8 billion running words. Empirical n-best precision evaluation methods are used to determine the best association measures (AMs) for extracting high frequency and meaningful FSs. The gold standard reference FSs list was developed in previous studies and manually evaluated against well-established quantitative and qualitative criteria. The results demonstrate that the MI.log{\_}f AM achieved the highest results in extracting significant FSs from the large MSA corpus, while the T-score association measure achieved the worst results.
Rule-based Automatic Multi-word Term Extraction and Lemmatization
Authors: Ranka Stankovi{\'c}, Cvetana Krstev, Ivan Obradovi{\'c}, Biljana Lazi{\'c}, Aleks Trtovac, ra
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: LEMMA, Lemmatization, Retrieval, Term Extraction

In this paper we present a rule-based method for multi-word term extraction that relies on extensive lexical resources in the form of electronic dictionaries and finite-state transducers for modelling various syntactic structures of multi-word terms. The same technology is used for lemmatization of extracted multi-word terms, which is unavoidable for highly inflected languages in order to pass extracted data to evaluators and subsequently to terminological e-dictionaries and databases. The approach is illustrated on a corpus of Serbian texts from the mining domain containing more than 600,000 simple word forms. Extracted and lemmatized multi-word terms are filtered in order to reject falsely offered lemmas and then ranked by introducing measures that combine linguistic and statistical information (C-Value, T-Score, LLR, and Keyness). Mean average precision for retrieval of MWU forms ranges from 0.789 to 0.804, while mean average precision of lemma production ranges from 0.956 to 0.960. The evaluation showed that 94{\%} of distinct multi-word forms were evaluated as proper multi-word units, and among them 97{\%} were associated with correct lemmas.
Distribution of Valency Complements in Czech Complex Predicates: Between Verb and Noun
Authors: V{\'a}clava Kettnerov{\'a}, Eduard Bej{\v{c}}ek
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper, we focus on Czech complex predicates formed by a light verb and a predicative noun expressed as the direct object. Although Czech ― as an inflectional language encoding syntactic relations via morphological cases ― provides an excellent opportunity to study the distribution of valency complements in the syntactic structure with complex predicates, this distribution has not been described so far. On the basis of a manual analysis of the richly annotated data from the Prague Dependency Treebank, we thus formulate principles governing this distribution. In an automatic experiment, we verify these principles on well-formed syntactic structures from the Prague Dependency Treebank and the Prague Czech-English Dependency Treebank with very satisfactory results: the distribution of 97{\%} of valency complements in the surface structure is governed by the proposed principles. These results corroborate that the surface structure formation of complex predicates is a regular process.
A Lexical Resource of Hebrew Verb-Noun Multi-Word Expressions
Authors: Chaya Liebeskind, Yaakov HaCohen-Kerner
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Natural Language Understanding, Translation

A verb-noun Multi-Word Expression (MWE) is a combination of a verb and a noun with or without other words, in which the combination has a meaning different from the meaning of the words considered separately. In this paper, we present a new lexical resource of Hebrew Verb-Noun MWEs (VN-MWEs). The VN-MWEs of this resource were manually collected and annotated from five different web resources. In addition, we analyze the lexical properties of Hebrew VN-MWEs by classifying them to three types: morphological, syntactic, and semantic. These two contributions are essential for designing algorithms for automatic VN-MWEs extraction. The analysis suggests some interesting features of VN-MWEs for exploration. The lexical resource enables to sample a set of positive examples for Hebrew VN-MWEs. This set of examples can either be used for training supervised algorithms or as seeds in unsupervised bootstrapping algorithms. Thus, this resource is a first step towards automatic identification of Hebrew VN-MWEs, which is important for natural language understanding, generation and translation systems.
Cross-lingual Linking of Multi-word Entities and their corresponding Acronyms
Authors: Guillaume Jacquet, Maud Ehrmann, Ralf Steinberger, Jaakko V{\"a}yrynen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Translation

This paper reports on an approach and experiments to automatically build a cross-lingual multi-word entity resource. Starting from a collection of millions of acronym/expansion pairs for 22 languages where expansion variants were grouped into monolingual clusters, we experiment with several aggregation strategies to link these clusters across languages. Aggregation strategies make use of string similarity distances and translation probabilities and they are based on vector space and graph representations. The accuracy of the approach is evaluated against Wikipedia{'}s redirection and cross-lingual linking tables. The resulting multi-word entity resource contains 64,000 multi-word entities with unique identifiers and their 600,000 multilingual lexical variants. We intend to make this new resource publicly available.
SemLinker, a Modular and Open Source Framework for Named Entity Discovery and Linking
Authors: Marie-Jean Meurs, Hayda Almeida, Ludovic Jean-Louis, Eric Charton
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, Knowledge Base Population, Position

This paper presents SemLinker, an open source system that discovers named entities, connects them to a reference knowledge base, and clusters them semantically. SemLinker relies on several modules that perform surface form generation, mutual disambiguation, entity clustering, and make use of two annotation engines. SemLinker was evaluated in the English Entity Discovery and Linking track of the Text Analysis Conference on Knowledge Base Population, organized by the US National Institute of Standards and Technology. Along with the SemLinker source code, we release our annotation files containing the discovered named entities, their types, and position across processed documents.
Context-enhanced Adaptive Entity Linking
Authors: Filip Ilievski, Giuseppe Rizzo, Marieke van Erp, Julien Plu, Rapha{\"e}l Troncy
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Entity Linking

More and more knowledge bases are publicly available as linked data. Since these knowledge bases contain structured descriptions of real-world entities, they can be exploited by entity linking systems that anchor entity mentions from text to the most relevant resources describing those entities. In this paper, we investigate adaptation of the entity linking task using contextual knowledge. The key intuition is that entity linking can be customized depending on the textual content, as well as on the application that would make use of the extracted information. We present an adaptive approach that relies on contextual knowledge from text to enhance the performance of ADEL, a hybrid linguistic and graph-based entity linking system. We evaluate our approach on a domain-specific corpus consisting of annotated WikiNews articles.
Entity Linking with a Paraphrase Flavor
Authors: Maria Pershina, Yifan He, Ralph Grishman
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, Diagnostic, Entity Disambiguation, Entity Linking

The task of Named Entity Linking is to link entity mentions in the document to their correct entries in a knowledge base and to cluster NIL mentions. Ambiguous, misspelled, and incomplete entity mention names are the main challenges in the linking process. We propose a novel approach that combines two state-of-the-art models ― for entity disambiguation and for paraphrase detection ― to overcome these challenges. We consider name variations as paraphrases of the same entity mention and adopt a paraphrase model for this task. Our approach utilizes a graph-based disambiguation model based on Personalized Page Rank, and then refines and clusters its output using the paraphrase similarity between entity mention strings. It achieves a competitive performance of 80.5{\%} in B3+F clustering score on diagnostic TAC EDL 2014 data.
Domain Adaptation for Named Entity Recognition Using CRFs
Authors: Tian Tian, Marco Dinarelli, Isabelle Tellier, Pedro Dias Cardoso
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Domain Adaptation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this paper we explain how we created a labelled corpus in English for a Named Entity Recognition (NER) task from multi-source and multi-domain data, for an industrial partner. We explain the specificities of this corpus with examples and describe some baseline experiments. We present some results of domain adaptation on this corpus using a labelled Twitter corpus (Ritter et al., 2011). We tested a semi-supervised method from (Garcia-Fernandez et al., 2014) combined with a supervised domain adaptation approach proposed in (Raymond and Fayolle, 2010) for machine learning experiments with CRFs (Conditional Random Fields). We use the same technique to improve the NER results on the Twitter corpus (Ritter et al., 2011). Our contributions thus consist in an industrial corpus creation and NER performance improvements.
IRIS: English-Irish Machine Translation System
Authors: Mihael Arcan, Caoilfhionn Lane, Eoin {\'O} Droighne{\'a}in, Paul Buitelaar
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Machine Translation, Translation

We describe IRIS, a statistical machine translation (SMT) system for translating from English into Irish and vice versa. Since Irish is considered an under-resourced language with a limited amount of machine-readable text, building a machine translation system that produces reasonable translations is rather challenging. As translation is a difficult task, current research in SMT focuses on obtaining statistics either from a large amount of parallel, monolingual or other multilingual resources. Nevertheless, we collected available English-Irish data and developed an SMT system aimed at supporting human translators and enabling cross-lingual language technology tasks.
Linguistically Inspired Language Model Augmentation for MT
Authors: George Tambouratzis, Vasiliki Pouli
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Language Modeling, Language Modelling, Machine Translation, model, Translation

The present article reports on efforts to improve the translation accuracy of a corpusâ€•based Machine Translation (MT) system. In order to achieve that, an error analysis performed on past translation outputs has indicated the likelihood of improving the translation accuracy by augmenting the coverage of the Target-Language (TL) side language model. The method adopted for improving the language model is initially presented, based on the concatenation of consecutive phrases. The algorithmic steps are then described that form the process for augmenting the language model. The key idea is to only augment the language model to cover the most frequent cases of phrase sequences, as counted over a TL-side corpus, in order to maximize the cases covered by the new language model entries. Experiments presented in the article show that substantial improvements in translation accuracy are achieved via the proposed method, when integrating the grown language model to the corpus-based MT system.
A Rule-based Shallow-transfer Machine Translation System for Scots and English
Authors: Gavin Abercrombie
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Cloze Test, Machine Translation, Translation

An open-source rule-based machine translation system is developed for Scots, a low-resourced minor language closely related to English and spoken in Scotland and Ireland. By concentrating on translation for assimilation (gist comprehension) from Scots to English, it is proposed that the development of dictionaries designed to be used with in the Apertium platform will be sufficient to produce translations that improve non-Scots speakers understanding of the language. Mono- and bilingual Scots dictionaries are constructed using lexical items gathered from a variety of resources across several domains. Although the primary goal of this project is translation for gisting, the system is evaluated for both assimilation and dissemination (publication-ready translations). A variety of evaluation methods are used, including a cloze test undertaken by human volunteers. While evaluation results are comparable to, and in some cases superior to, those of other language pairs within the Apertium platform, room for improvement is identified in several areas of the system.
Syntax-based Multi-system Machine Translation
Authors: Mat{\=\i}ss Rikters, Inguna Skadi{\c{n}}a
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Language Modeling, Language Modelling, Machine Translation, Sentence, Translation

This paper describes a hybrid machine translation system that explores a parser to acquire syntactic chunks of a source sentence, translates the chunks with multiple online machine translation (MT) system application program interfaces (APIs) and creates output by combining translated chunks to obtain the best possible translation. The selection of the best translation hypothesis is performed by calculating the perplexity for each translated chunk. The goal of this approach is to enhance the baseline multi-system hybrid translation (MHyT) system that uses only a language model to select best translation from translations obtained with different APIs and to improve overall English â€• Latvian machine translation quality over each of the individual MT APIs. The presented syntax-based multi-system translation (SyMHyT) system demonstrates an improvement in terms of BLEU and NIST scores compared to the baseline system. Improvements reach from 1.74 up to 2.54 BLEU points.