pwc-archive/papers-with-abstracts huggingface dataset:
The CMU METAL Farsi NLP Approach
Authors: Weston Feely, Mehdi Manshadi, Robert Frederking, Lori Levin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing

While many high-quality tools are available for analyzing major languages such as English, equivalent freely-available tools for important but lower-resourced languages such as Farsi are more difficult to acquire and integrate into a useful NLP front end. We report here on an accurate and efficient Farsi analysis front end that we have assembled, which may be useful to others who wish to work with written Farsi. The pre-existing components and resources that we incorporated include the Carnegie Mellon TurboParser and TurboTagger (Martins et al., 2010) trained on the Dadegan Treebank (Rasooli et al., 2013), the Uppsala Farsi text normalizer PrePer (Seraji, 2013), the Uppsala Farsi tokenizer (Seraji et al., 2012a), and Jon DehdariÂ’s PerStem (Jadidinejad et al., 2010). This set of tools (combined with additional normalization and tokenization modules that we have developed and made available) achieves a dependency parsing labeled attachment score of 89.49{\%}, unlabeled attachment score of 92.19{\%}, and label accuracy score of 91.38{\%} on a held-out parsing test data set. All of the components and resources used are freely available. In addition to describing the components and resources, we also explain the rationale for our choices.
Recognising suicidal messages in Dutch social media
Authors: Bart Desmet, V{\'e}ronique Hoste
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: General Classification, LEMMA

Early detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the automatic recognition of suicidal messages in social media. We present experiments for automatically detecting relevant messages (with suicide-related content), and those containing suicide threats. A sample of 1357 texts was annotated in a corpus of 2674 blog posts and forum messages from Netlog, indicating relevance, origin, severity of suicide threat and risks as well as protective factors. For the classification experiments, Naive Bayes, SVM and KNN algorithms are combined with shallow features, i.e. bag-of-words of word, lemma and character ngrams, and post length. The best relevance classification is achieved by using SVM with post length, lemma and character ngrams, resulting in an F-score of 85.6{\%} (78.7{\%} precision and 93.8{\%} recall). For the second task (threat detection), a cascaded setup which first filters out irrelevant messages with SVM and then predicts the severity with KNN, performs best: 59.2{\%} F-score (69.5{\%} precision and 51.6{\%} recall).
A Rank-based Distance Measure to Detect Polysemy and to Determine Salient Vector-Space Features for German Prepositions
Authors: Maximilian K{\"o}per, Sabine Schulte im Walde
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper addresses vector space models of prepositions, a notoriously ambiguous word class. We propose a rank-based distance measure to explore the vector-spatial properties of the ambiguous objects, focusing on two research tasks: (i) to distinguish polysemous from monosemous prepositions in vector space; and (ii) to determine salient vector-space features for a classification of preposition senses. The rank-based measure predicts the polysemy vs. monosemy of prepositions with a precision of up to 88{\%}, and suggests preposition-subcategorised nouns as more salient preposition features than preposition-subcategorising verbs.
Expanding n-gram analytics in ELAN and a case study for sign synthesis
Authors: Rosalee Wolfe, John McDonald, Larwan Berke, Marie Stumbo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Corpus analysis is a powerful tool for signed language synthesis. A new extension to ELAN offers expanded n-gram analysis tools including improved search capabilities and an extensive library of statistical measures of association for n-grams. Uncovering and exploring coarticulatory timing effects via corpus analysis requires n-gram analysis to discover the most frequently occurring bigrams. This paper presents an overview of the new tools and a case study in American Sign Language synthesis that exploits these capabilities for computing more natural timing in generated sentences. The new extension provides a time-saving convenience for language researchers using ELAN.
Sentence Rephrasing for Parsing Sentences with OOV Words
Authors: Hen-Hsen Huang, Huan-Yuan Chen, Chang-Sheng Yu, Hsin-Hsi Chen, Po-Ching Lee, Chun-Hsun Chen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dependency Parsing, Domain Adaptation, Information Retrieval, Machine Translation, Part-Of-Speech Tagging, Retrieval, Sentence

This paper addresses the problems of out-of-vocabulary (OOV) words, named entities in particular, in dependency parsing. The OOV words, whose word forms are unknown to the learning-based parser, in a sentence may decrease the parsing performance. To deal with this problem, we propose a sentence rephrasing approach to replace each OOV word in a sentence with a popular word of the same named entity type in the training set, so that the knowledge of the word forms can be used for parsing. The highest-frequency-based rephrasing strategy and the information-retrieval-based rephrasing strategy are explored to select the word to replace, and the Chinese Treebank 6.0 (CTB6) corpus is adopted to evaluate the feasibility of the proposed sentence rephrasing strategies. Experimental results show that rephrasing some specific types of OOV words such as Corporation, Organization, and Competition increases the parsing performances. This methodology can be applied to domain adaptation to deal with OOV problems.
Mapping the Lexique des Verbes du Fran\ccais (Lexicon of French Verbs) to a NLP lexicon using examples
Authors: Bruno Guillaume, Kar{\"e}n Fort, Guy Perrier, Paul B{\'e}daride
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This article presents experiments aiming at mapping the Lexique des Verbes du Fran{\c{c}}ais (Lexicon of French Verbs) to FRILEX, a Natural Language Processing (NLP) lexicon based on D ICOVALENCE. The two resources (Lexicon of French Verbs and D ICOVALENCE) were built by linguists, based on very different theories, which makes a direct mapping nearly impossible. We chose to use the examples provided in one of the resource to find implicit links between the two and make them explicit.
Language Resources for French in the Biomedical Domain
Authors: Aur{\'e}lie N{\'e}v{\'e}ol, Julien Grosjean, St{\'e}fan Darmoni, Pierre Zweigenbaum
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The biomedical domain offers a wealth of linguistic resources for Natural Language Processing, including terminologies and corpora. While many of these resources are prominently available for English, other languages including French benefit from substantial coverage thanks to the contribution of an active community over the past decades. However, access to terminological resources in languages other than English may not be as straight-forward as access to their English counterparts. Herein, we review the extent of resource coverage for French and give pointers to access French-language resources. We also discuss the sources and methods for making additional material available for French.
The MERLIN corpus: Learner language and the CEFR
Authors: Adriane Boyd, Jirka Hana, Lionel Nicolas, Detmar Meurers, Katrin Wisniewski, Andrea Abel, Karin Sch{\"o}ne, Barbora {\v{S}}tindlov{\'a}, Chiara Vettori
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Acquisition, Language Identification, Native Language Identification

The MERLIN corpus is a written learner corpus for Czech, German,and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains 2,290 learner texts produced in standardized language certifications covering CEFR levels A1-C1. The MERLIN annotation scheme includes a wide range of language characteristics that enable research into the empirical foundations of the CEFR scales and provide language teachers, test developers, and Second Language Acquisition researchers with concrete examples of learner performance and progress across multiple proficiency levels. For computational linguistics, it provide a range of authentic learner data for three target languages, supporting a broadening of the scope of research in areas such as automatic proficiency classification or native language identification. The annotated corpus and related information will be freely available as a corpus resource and through a freely accessible, didactically-oriented online platform.
Computer-aided morphology expansion for Old Swedish
Authors: Yvonne Adesam, Malin Ahlberg, Peter Andersson, Gerlof Bouma, Markus Forsberg, Mans Hulden
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper we describe and evaluate a tool for paradigm induction and lexicon extraction that has been applied to Old Swedish. The tool is semi-supervised and uses a small seed lexicon and unannotated corpora to derive full inflection tables for input lemmata. In the work presented here, the tool has been modified to deal with the rich spelling variation found in Old Swedish texts. We also present some initial experiments, which are the first steps towards creating a large-scale morphology for Old Swedish.
Two-Step Machine Translation with Lattices
Authors: Bushra Jawaid, Ond{\v{r}}ej Bojar
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Modelling, Lemmatization, Machine Translation, Translation, Vocal Bursts Valence Prediction

The idea of two-step machine translation was introduced to divide the complexity of the search space into two independent steps: (1) lexical translation and reordering, and (2) conjugation and declination in the target language. In this paper, we extend the two-step machine translation structure by replacing state-of-the-art phrase-based machine translation with the hierarchical machine translation in the 1st step. We further extend the fixed string-based input format of the 2nd step with word lattices (Dyer et al., 2008); this provides the 2nd step with the opportunity to choose among a sample of possible reorderings instead of relying on the single best one as produced by the 1st step.
The Munich Biovoice Corpus: Effects of Physical Exercising, Heart Rate, and Skin Conductance on Human Speech Production
Authors: Bj{\"o}rn Schuller, Felix Friedmann, Florian Eyben
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Binary Classification, Heart rate estimation

We introduce a spoken language resource for the analysis of impact that physical exercising has on human speech production. In particular, the database provides heart rate and skin conductance measurement information alongside the audio recordings. It contains recordings from 19 subjects in a relaxed state and after exercising. The audio material includes breathing, sustained vowels, and read text. Further, we describe pre-extracted audio-features from our openSMILE feature extractor together with baseline performances for the recognition of high and low heart rate using these features. The baseline results clearly show the feasibility of automatic estimation of heart rate from the human voice, in particular from sustained vowels. Both regression - in order to predict the exact heart rate value - and a binary classification setting for high and low heart rate classes are investigated. Finally, we give tendencies on feature group relevance in the named contexts of heart rate estimation and skin conductivity estimation.
DysList: An Annotated Resource of Dyslexic Errors
Authors: Luz Rello, Ricardo Baeza-Yates, Joaquim Llisterri
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Reading Comprehension

We introduce a language resource for Spanish, DysList, composed of a list of unique errors extracted from a collection of texts written by people with dyslexia. Each of the errors was annotated with a set of characteristics as well as visual and phonetic features. To the best of our knowledge this is the largest resource of this kind, especially given the difficulty of finding texts written by people with dyslexia
Estimation of Speaking Style in Speech Corpora Focusing on speech transcriptions
Authors: Raymond Shen, Hideaki Kikuchi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Retrieval

Recent developments in computer technology have allowed the construction and widespread application of large-scale speech corpora. To foster ease of data retrieval for people interested in utilising these speech corpora, we attempt to characterise speaking style across some of them. In this paper, we first introduce the 3 scales of speaking style proposed by Eskenazi in 1993. We then use morphological features extracted from speech transcriptions that have proven effective in style discrimination and author identification in the field of natural language processing to construct an estimation model of speaking style. More specifically, we randomly choose transcriptions from various speech corpora as text stimuli with which to conduct a rating experiment on speaking style perception; then, using the features extracted from those stimuli and the rating results, we construct an estimation model of speaking style by a multi-regression analysis. After the cross validation (leave-1-out), the results show that among the 3 scales of speaking style, the ratings of 2 scales can be estimated with high accuracies, which prove the effectiveness of our method in the estimation of speaking style.
Evaluation of different strategies for domain adaptation in opinion mining
Authors: Garcia-Fern, Anne ez, Olivier Ferret, Marco Dinarelli
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Opinion Mining, Sentiment Analysis, Text Classification

The work presented in this article takes place in the field of opinion mining and aims more particularly at finding the polarity of a text by relying on machine learning methods. In this context, it focuses on studying various strategies for adapting a statistical classifier to a new domain when training data only exist for one or several other domains. This study shows more precisely that a self-training procedure consisting in enlarging the initial training corpus with texts from the target domain that were reliably classified by the classifier is the most successful and stable strategy for the tested domains. Moreover, this strategy gets better results in most cases than (Blitzer et al., 2007){'}s method on the same evaluation corpus while it is more simple.
Clinical Data-Driven Probabilistic Graph Processing
Authors: Travis Goodwin, S Harabagiu, a
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval

Electronic Medical Records (EMRs) encode an extraordinary amount of medical knowledge. Collecting and interpreting this knowledge, however, belies a significant level of clinical understanding. Automatically capturing the clinical information is crucial for performing comparative effectiveness research. In this paper, we present a data-driven approach to model semantic dependencies between medical concepts, qualified by the beliefs of physicians. The dependencies, captured in a patient cohort graph of clinical pictures and therapies is further refined into a probabilistic graphical model which enables efficient inference of patient-centered treatment or test recommendations (based on probabilities). To perform inference on the graphical model, we describe a technique of smoothing the conditional likelihood of medical concepts by their semantically-similar belief values. The experimental results, as compared against clinical guidelines are very promising.
Comparing Similarity Measures for Distributional Thesauri
Authors: Muntsa Padr{\'o}, Marco Idiart, Aline Villavicencio, Carlos Ramisch
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Dimensionality Reduction

Distributional thesauri have been applied for a variety of tasks involving semantic relatedness. In this paper, we investigate the impact of three parameters: similarity measures, frequency thresholds and association scores. We focus on the robustness and stability of the resulting thesauri, measuring inter-thesaurus agreement when testing different parameter values. The results obtained show that low-frequency thresholds affect thesaurus quality more than similarity measures, with more agreement found for increasing thresholds.These results indicate the sensitivity of distributional thesauri to frequency. Nonetheless, the observed differences do not transpose over extrinsic evaluation using TOEFL-like questions. While this may be specific to the task, we argue that a careful examination of the stability of distributional resources prior to application is needed.
Pruning the Search Space of the Wolof LFG Grammar Using a Probabilistic and a Constraint Grammar Parser
Authors: Cheikh M. Bamba Dione
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Morphological Analysis, Morphological Disambiguation

This paper presents a method for greatly reducing parse times in LFG by integrating a Constraint Grammar parser into a probabilistic context-free grammar. The CG parser is used in the pre-processing phase to reduce morphological and lexical ambiguity. Similarly, the c-structure pruning mechanism of XLE is used in the parsing phase to discard low-probability c-structures, before f-annotations are solved. The experiment results show a considerable increase in parsing efficiency and robustness in the annotation of Wolof running text. The Wolof CG parser indicated an f-score of 90{\%} for morphological disambiguation and a speedup of ca. 40{\%}, while the c-structure pruning method increased the speed of the Wolof grammar by over 36{\%}. On a small amount of data, CG disambiguation and c-structure pruning allowed for a speedup of 58{\%}, however with a substantial drop in parse accuracy of 3.62.
AraNLP: a Java-based Library for the Processing of Arabic Text.
Authors: Maha Althobaiti, Udo Kruschwitz, Massimo Poesio
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, POS, Sentence, Transliteration

We present a free, Java-based library named {``}AraNLP{''} that covers various Arabic text preprocessing tools. Although a good number of tools for processing Arabic text already exist, integration and compatibility problems continually occur. AraNLP is an attempt to gather most of the vital Arabic text preprocessing tools into one library that can be accessed easily by integrating or accurately adapting existing tools and by developing new ones when required. The library includes a sentence detector, tokenizer, light stemmer, root stemmer, part-of speech tagger (POS-tagger), word segmenter, normalizer, and a punctuation and diacritic remover.
Criteria for Identifying and Annotating Caused Motion Constructions in Corpus Data
Authors: Jena D. Hwang, Annie Zaenen, Martha Palmer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Sentence

While natural language processing performance has been improved through the recognition that there is a relationship between the semantics of the verb and the syntactic context in which the verb is realized, sentences where the verb does not conform to the expected syntax-semantic patterning behavior remain problematic. For example, in the sentence The crowed laughed the clown off the stage, a verb of non-verbal communication laugh is used in a caused motion construction and gains a motion entailment that is atypical given its inherent lexical semantics. This paper focuses on our efforts at defining the semantic types and varieties of caused motion constructions (CMCs) through an iterative annotation process and establishing annotation guidelines based on these criteria to aid in the production of a consistent and reliable annotation. The annotation will serve as training and test data for classifiers for CMCs, and the CMC definitions developed throughout this study will be used in extending VerbNet to handle representations of sentences in which a verb is used in a syntactic context that is atypical for its lexical semantics.
Web-imageability of the Behavioral Features of Basic-level Concepts
Authors: Yoshihiko Hayashi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Form, Information Retrieval

The recent research direction toward multimodal semantic representation would be further advanced, if we could have a machinery to collect adequate images from the Web, given a target concept. With this motivation, this paper particularly investigates into the Web imageabilities of the behavioral features (e.g. beaver builds dams) of a basic-level concept (beaver). The term Web-imageability denotes how adequately the images acquired from the Web deliver the intended meaning of a complex concept. The primary contributions made in this paper are twofold: (1) beaver building dams-type queries can better yield relevant Web images, suggesting that the present participle form (-ing form) of a verb (building), as a query component, is more effective than the base form; (2) the behaviors taken by animate beings are likely to be more depicted on the Web, particularly if the behaviors are, in a sense, inherent to animate beings (e.g.,motion, consumption), while the creation-type behaviors of inanimate beings are not. The paper further analyzes linguistic annotations that were independently given to some of the images, and discusses an aspect of the semantic gap between image and language.
The Alveo Virtual Laboratory: A Web Based Repository API
Authors: Steve Cassidy, Dominique Estival, Timothy Jones, Denis Burnham, Jared Burghold
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The Human Communication Science Virtual Laboratory (HCS vLab) is an eResearch project funded under the Australian Government NeCTAR program to build a platform for collaborative eResearch around data representing human communication and the tools that researchers use in their analysis. The human communication science field is broadly defined to encompass the study of language from various perspectives but also includes research on music and various other forms of human expression. This paper outlines the core architecture of the HCS vLab and in particular, highlights the web based API that provides access to data and tools to authenticated users.
A Compact Interactive Visualization of Dependency Treebank Query Results
Authors: Chris Culy, Marco Passarotti, Ulla K{\"o}nig-Cardanobile
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

One of the challenges of corpus querying is making sense of the results of a query, especially when a large number of results and linguistically annotated data are concerned. While the most widespread tools for querying syntactically annotated corpora tend to focus on single occurrences, one aspect that is not fully exploited yet in this area is that language is a complex system whose units are connected to each other at both microscopic (the single occurrences) and macroscopic level (the whole system itself). Assuming that language is a system, we describe a tool (using the DoubleTreeJS visualization) to visualize the results of querying dependency treebanks by forming a node from a single item type, and building a network in which the heads and the dependents of the central node are respectively the left and the right vertices of the tree, which are connected to the central node by dependency relations. One case study is presented, consisting in the exploitation of DoubleTreeJS for supporting one assumption in theoretical linguistics with evidence provided by the data of a dependency treebank of Medieval Latin.