pwc-archive/papers-with-abstracts huggingface dataset:
ELRA's Consolidated Services for the HLT Community
Authors: Victoria Arranz, Khalid Choukri, Val{\'e}rie Mapelli, H{\'e}l{\`e}ne Mazo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper emphasises on ELRAs contribution to the HLT field thanks to the consolidation of its services since LREC 2012. Among the most recent contributions is the establishment of the International Standard Language Resource Number (ISLRN), with the creation and exploitation of an associated web portal to enable the procurement of unique identifiers for Language Resources. Interoperability, consolidation and synchronization remain also a strong focus in ELRAs cataloguing work, in particular with ELRAs involvement in the META-SHARE project, whose platform is to become ELRAs next instrument of sharing LRs. Since last LREC, ELRA has continued its action to offer free LRs to the research community. Cooperation is another watchword within ELRAs activities on multiple aspects: 1) at the legal level, ELRA is supporting the EC in identifying the gaps to be fulfilled to reach harmonized copyright regulations for the HLT community in Europe; 2) at the production level, ELRA is participating in several international projects, in the field of LR production and evaluation of technologies; 3) at the communication level, ELRA has organised the NLP12 meeting with the aim of boosting co-operation and strengthening the bridges between various communities.
Single Classifier Approach for Verb Sense Disambiguation based on Generalized Features
Authors: Daisuke Kawahara, Martha Palmer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Semantic Parsing, Word Sense Disambiguation

We present a supervised method for verb sense disambiguation based on VerbNet. Most previous supervised approaches to verb sense disambiguation create a classifier for each verb that reaches a frequency threshold. These methods, however, have a significant practical problem that they cannot be applied to rare or unseen verbs. In order to overcome this problem, we create a single classifier to be applied to rare or unseen verbs in a new text. This single classifier also exploits generalized semantic features of a verb and its modifiers in order to better deal with rare or unseen verbs. Our experimental results show that the proposed method achieves equivalent performance to per-verb classifiers, which cannot be applied to unseen verbs. Our classifier could be utilized to improve the classifications in lexical resources of verbs, such as VerbNet, in a semi-automatic manner and to possibly extend the coverage of these resources to new verbs.
Extracting semantic relations from Portuguese corpora using lexical-syntactic patterns
Authors: Raquel Amaro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Word Sense Disambiguation

The growing investment on automatic extraction procedures, together with the need for extensive resources, makes semi-automatic construction a new viable and efficient strategy for developing of language resources, combining accuracy, size, coverage and applicability. These assumptions motivated the work depicted in this paper, aiming at the establishment and use of lexical-syntactic patterns for extracting semantic relations for Portuguese from corpora, part of a larger ongoing project for the semi-automatic extension of WordNet.PT. 26 lexical-syntactic patterns were established, covering hypernymy/hyponymy and holonymy/meronymy relations between nominal items, and over 34 000 contexts were manually analyzed to evaluate the productivity of each pattern. The set of patterns and respective examples are given, as well as data concerning the extraction of relations - right hits, wrong hits and related hits-, and the total of occurrences of each pattern in CPRC. Although language-dependent, and thus clearly of obvious interest for the development of lexical resources for Portuguese, the results depicted in this paper are also expected to be helpful as a basis for the establishment of patterns for related languages such as Spanish, Catalan, French or Italian.
LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain
Authors: Artem Ostankov, Florian R{\"o}hrbein, Ulli Waltinger
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Predicate Detection, Question Answering, Sentence

This paper presents Linked Health Answers, a natural language question answering systems that utilizes health data drawn from the Linked Data Cloud. The contributions of this paper are three-fold: Firstly, we review existing state-of-the-art NLP platforms and components, with a special focus on components that allow or support an automatic SPARQL construction. Secondly, we present the implemented architecture of the Linked Health Answers systems. Thirdly, we propose an statistical bootstrap approach for the identification and disambiguation of RDF-based predicates using a machine learning-based classifier. The evaluation focuses on predicate detection in sentence statements, as well as within the scenario of natural language questions.
An analysis of ambiguity in word sense annotations
Authors: David Jurgens
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Word Sense Disambiguation

Word sense annotation is a challenging task where annotators distinguish which meaning of a word is present in a given context. In some contexts, a word usage may elicit multiple interpretations, resulting either in annotators disagreeing or in allowing the usage to be annotated with multiple senses. While some works have allowed the latter, the extent to which multiple sense annotations are needed has not been assessed. The present work analyzes a dataset of instances annotated with multiple WordNet senses to assess the causes of the multiple interpretations and their relative frequencies, along with the effect of the multiple senses on the contextual interpretation. We show that contextual underspecification is the primary cause of multiple interpretations but that syllepsis still accounts for more than a third of the cases. In addition, we show that sense coarsening can only partially remove the need for labeling instances with multiple senses and we provide suggestions for how future sense annotation guidelines might be developed to account for this need.
VOLIP: a corpus of spoken Italian and a virtuous example of reuse of linguistic resources
Authors: Iol Alfano, a, Francesco Cutugno, Aurelio De Rosa, Claudio Iacobini, Renata Savy, Miriam Voghera
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The corpus VoLIP (The Voice of LIP) is an Italian speech resource which associates the audio signals to the orthographic transcriptions of the LIP Corpus. The LIP Corpus was designed to represent diaphasic, diatopic and diamesic variation. The Corpus was collected in the early Â‘90s to compile a frequency lexicon of spoken Italian and its size was tailored to produce a reliable frequency lexicon for the first 3,000 lemmas. Therefore, it consists of about 500,000 word tokens for 60 hours of recording. The speech materials belong to five different text registers and they were collected in four different cities. Thanks to a modern technological approach VoLIP web service allows users to search the LIP corpus using IMDI metadata, lexical or morpho-syntactic entry keys, receiving as result the audio portions aligned to the corresponding required entry. The VoLIP corpus is freely available at the URL http://www.parlaritaliano.it.
Chasing the Perfect Splitter: A Comparison of Different Compound Splitting Tools
Authors: Carla Parra Escart{\'\i}n
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

This paper reports on the evaluation of two compound splitters for German. Compounding is a very frequent phenomenon in German and thus efficient ways of detecting and correctly splitting compound words are needed for natural language processing applications. This paper presents different strategies for compound splitting, focusing on German. Four compound splitters for German are presented. Two of them were used in Statistical Machine Translation (SMT) experiments, obtaining very similar qualitative scores in terms of BLEU and TER and therefore a thorough evaluation of both has been carried out.
A Comparison of MT Errors and ESL Errors
Authors: Homa B. Hashemi, Rebecca Hwa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

Generating fluent and grammatical sentences is a major goal for both Machine Translation (MT) and second-language Grammar Error Correction (GEC), but there have not been a lot of cross-fertilization between the two research communities. Arguably, an automatic translate-to-English system might be seen as an English as a Second Language (ESL) writer whose native language is the source language. This paper investigates whether research findings from the GEC community may help with characterizing MT error analysis. We describe a method for the automatic classification of MT errors according to English as a Second Language (ESL) error categories and conduct a large comparison experiment that includes both high-performing and low-performing translate-to-English MT systems for several source languages. Comparing the distribution of MT error types for all the systems suggests that MT systems have fairly similar distributions regardless of their source languages, and the high-performing MT systems have error distributions that are more similar to those of the low-performing MT systems than to those of ESL learners with the same L1.
New functions for a multipurpose multimodal tool for phonetic and linguistic analysis of very large speech corpora
Authors: Philippe Martin
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: EEG, Electroencephalogram (EEG), Speech Recognition

The increased interest for linguistic analysis of spontaneous (i.e. non-prepared) speech from various points of view (semantic, syntactic, morphologic, phonologic and intonative) lead to the development of ever more sophisticated dedicated tools. Although the software Praat emerged as the de facto standard for the analysis of spoken data, its use for intonation studies is often felt as not optimal, notably for its limited capabilities in fundamental frequency tracking. This paper presents some of the recently implemented features of the software WinPitch, developed with the analysis of spontaneous speech in mind (and notably for the C-ORAL-ROM project 10 years ago). Among many features, WinPitch includes a set of multiple pitch tracking algorithms aimed to obtain reliable pitch curves in adverse recording conditions (echo, filtering, poor signal to noise ratio, etc.). Others functions of WinPitch incorporate an integrated concordancer, an on the fly text-sound aligner, and routines for EEG analysis.
Hot Topics and Schisms in NLP: Community and Trend Analysis with Saffron on ACL and LREC Proceedings
Authors: Paul Buitelaar, Georgeta Bordea, Barry Coughlan
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Machine Translation, Term Extraction, Text Classification

In this paper we present a comparative analysis of two series of conferences in the field of Computational Linguistics, the LREC conference and the ACL conference. Conference proceedings were analysed using Saffron by performing term extraction and topical hierarchy construction with the goal of analysing topic trends and research communities. The system aims to provide insight into a research community and to guide publication and participation strategies, especially of novice researchers.
The American Local News Corpus
Authors: Ann Irvine, Joshua Langfus, Chris Callison-Burch
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present the American Local News Corpus (ALNC), containing over 4 billion words of text from 2,652 online newspapers in the United States. Each article in the corpus is associated with a timestamp, state, and city. All 50 U.S. states and 1,924 cities are represented. We detail our method for taking daily snapshots of thousands of local and national newspapers and present two example corpus analyses. The first explores how different sports are talked about over time and geography. The second compares per capita murder rates with news coverage of murders across the 50 states. The ALNC is about the same size as the Gigaword corpus and is growing continuously. Version 1.0 is available for research use.
HamleDT 2.0: Thirty Dependency Treebanks Stanfordized
Authors: Rudolf Rosa, Jan Ma{\v{s}}ek, David Mare{\v{c}}ek, Martin Popel, Daniel Zeman, Zden{\v{e}}k {\v{Z}}abokrtsk{\'y}
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

We present HamleDT 2.0 (HArmonized Multi-LanguagE Dependency Treebank). HamleDT 2.0 is a collection of 30 existing treebanks harmonized into a common annotation style, the Prague Dependencies, and further transformed into Stanford Dependencies, a treebank annotation style that became popular in recent years. We use the newest basic Universal Stanford Dependencies, without added language-specific subtypes. We describe both of the annotation styles, including adjustments that were necessary to make, and provide details about the conversion process. We also discuss the differences between the two styles, evaluating their advantages and disadvantages, and note the effects of the differences on the conversion. We regard the stanfordization as generally successful, although we admit several shortcomings, especially in the distinction between direct and indirect objects, that have to be addressed in future. We release part of HamleDT 2.0 freely; we are not allowed to redistribute the whole dataset, but we do provide the conversion pipeline.
Building The Sense-Tagged Multilingual Parallel Corpus
Authors: Shan Wang, Francis Bond
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

Sense-annotated parallel corpora play a crucial role in natural language processing. This paper introduces our progress in creating such a corpus for Asian languages using English as a pivot, which is the first such corpus for these languages. Two sets of tools have been developed for sequential and targeted tagging, which are also easy to set up for any new language in addition to those we are annotating. This paper also briefly presents the general guidelines for doing this project. The current results of monolingual sense-tagging and multilingual linking are illustrated, which indicate the differences among genres and language pairs. All the tools, guidelines and the manually annotated corpus will be freely available at compling.ntu.edu.sg/ntumc.
Multilingual corpora with coreferential annotation of person entities
Authors: Marcos Garcia, Pablo Gamallo
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: coreference-resolution, Coreference Resolution, Open Information Extraction, Relation Extraction, Text Summarization

This paper presents three corpora with coreferential annotation of person entities for Portuguese, Galician and Spanish. They contain coreference links between several types of pronouns (including elliptical, possessive, indefinite, demonstrative, relative and personal clitic and non-clitic pronouns) and nominal phrases (including proper nouns). Some statistics have been computed, showing distributional aspects of coreference both in journalistic and in encyclopedic texts. Furthermore, the paper shows the importance of coreference resolution for a task such as Information Extraction, by evaluating the output of an Open Information Extraction system on the annotated corpora. The corpora are freely distributed in two formats: (i) the SemEval-2010 and (ii) the brat rapid annotation tool, so they can be enlarged and improved collaboratively.
SANA: A Large Scale Multi-Genre, Multi-Dialect Lexicon for Arabic Subjectivity and Sentiment Analysis
Authors: Muhammad Abdul-Mageed, Mona Diab
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Arabic Sentiment Analysis, Machine Translation, Sentiment Analysis

The computational treatment of subjectivity and sentiment in natural language is usually significantly improved by applying features exploiting lexical resources where entries are tagged with semantic orientation (e.g., positive, negative values). In spite of the fair amount of work on Arabic sentiment analysis over the past few years (e.g., (Abbasi et al., 2008; Abdul-Mageed et al., 2014; Abdul-Mageed et al., 2012; Abdul-Mageed and Diab, 2012a; Abdul-Mageed and Diab, 2012b; Abdul-Mageed et al., 2011a; Abdul-Mageed and Diab, 2011)), the language remains under-resourced as to these polarity repositories compared to the English language. In this paper, we report efforts to build and present SANA, a large-scale, multi-genre, multi-dialect multi-lingual lexicon for the subjectivity and sentiment analysis of the Arabic language and dialects.
Evaluation of Technology Term Recognition with Random Indexing
Authors: Behrang Zadeh, H, Siegfried schuh
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper, we propose a method that combines the principles of automatic term recognition and the distributional hypothesis to identify technology terms from a corpus of scientific publications. We employ the random indexing technique to model terms{'} surrounding words, which we call the context window, in a vector space at reduced dimension. The constructed vector space and a set of reference vectors, which represents manually annotated technology terms, in a k-nearest-neighbour voting classification scheme are used for term classification. In this paper, we examine a number of parameters that influence the obtained results. First, we inspect several context configurations, i.e. the effect of the context window size, the direction in which co-occurrence counts are collected, and information about the order of words within the context windows. Second, in the k-nearest-neighbour voting scheme, we study the role that neighbourhood size selection plays, i.e. the value of k. The obtained results are similar to word space models. The performed experiments suggest the best performing context are small (i.e. not wider than 3 words), are extended in both directions and encode the word order information. Moreover, the accomplished experiments suggest that the obtained results, to a great extent, are independent of the value of k.
Optimizing a Distributional Semantic Model for the Prediction of German Particle Verb Compositionality
Authors: Stefan Bott, Sabine Schulte im Walde
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Lemmatization, Semantic Composition

In the work presented here we assess the degree of compositionality of German Particle Verbs with a Distributional Semantics Model which only relies on word window information and has no access to syntactic information as such. Our method only takes the lexical distributional distance between the Particle Verb to its Base Verb as a predictor for compositionality. We show that the ranking of distributional similarity correlates significantly with the ranking of human judgements on semantic compositionality for a series of Particle Verbs and the Base Verbs they are derived from. We also investigate the influence of further linguistic factors, such as the ambiguity and the overall frequency of the verbs and a syntactically separate occurrences of verbs and particles that causes difficulties for the correct lemmatization of Particle Verbs. We analyse in how far these factors may influence the success with which the compositionality of the Particle Verbs may be predicted.
A Hindi-English Code-Switching Corpus
Authors: Anik Dey, Pascale Fung
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The aim of this paper is to investigate the rules and constraints of code-switching (CS) in Hindi-English mixed language data. In this paper, weÂ’ll discuss how we collected the mixed language corpus. This corpus is primarily made up of student interview speech. The speech was manually transcribed and verified by bilingual speakers of Hindi and English. The code-switching cases in the corpus are discussed and the reasons for code-switching are explained.
The Language Application Grid
Authors: Nancy Ide, James Pustejovsky, Christopher Cieri, Eric Nyberg, Di Wang, Keith Suderman, Marc Verhagen, Jonathan Wright
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Question Answering, Translation

The Language Application (LAPPS) Grid project is establishing a framework that enables language service discovery, composition, and reuse and promotes sustainability, manageability, usability, and interoperability of natural language Processing (NLP) components. It is based on the service-oriented architecture (SOA), a more recent, web-oriented version of the pipeline architecture that has long been used in NLP for sequencing loosely-coupled linguistic analyses. The LAPPS Grid provides access to basic NLP processing tools and resources and enables pipelining such tools to create custom NLP applications, as well as composite services such as question answering and machine translation together with language resources such as mono- and multi-lingual corpora and lexicons that support NLP. The transformative aspect of the LAPPS Grid is that it orchestrates access to and deployment of language resources and processing functions available from servers around the globe and enables users to add their own language resources, services, and even service grids to satisfy their particular needs.
Integration of Workflow and Pipeline for Language Service Composition
Authors: Trang Mai Xuan, Yohei Murakami, Donghui Lin, Toru Ishida
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Service Composition

Integrating language resources and language services is a critical part of building natural language processing applications. Service workflow and processing pipeline are two approaches for sharing and combining language resources. Workflow languages focus on expressive power of the languages to describe variety of workflow patterns to meet users{'} needs. Users can combine those language services in service workflows to meet their requirements. The workflows can be accessible in distributed manner and can be invoked independently of the platforms. However, workflow languages lack of pipelined execution support to improve performance of workflows. Whereas, the processing pipeline provides a straightforward way to create a sequence of linguistic processing to analyze large amounts of text data. It focuses on using pipelined execution and parallel execution to improve throughput of pipelines. However, the resulting pipelines are standalone applications, i.e., software tools that are accessible only via local machine and that can only be run with the processing pipeline platforms. In this paper we propose an integration framework of the two approaches so that each offests the disadvantages of the other. We then present a case study wherein two representative frameworks, the Language Grid and UIMA, are integrated.
Segmentation evaluation metrics, a comparison grounded on prosodic and discourse units
Authors: Klim Peshkov, Laurent Pr{\'e}vot
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Discourse Segmentation, Information Retrieval, Segmentation

Knowledge on evaluation metrics and best practices of using them have improved fast in the recent years Fort et al. (2012). However, the advances concern mostly evaluation of classification related tasks. Segmentation tasks have received less attention. Nevertheless, there are crucial in a large number of linguistic studies. A range of metrics is available (F-score on boundaries, F-score on units, WindowDiff ((WD), Boundary Similarity (BS) but it is still relatively difficult to interpret these metrics on various linguistic segmentation tasks, such as prosodic and discourse segmentation. In this paper, we consider real segmented datasets (introduced in Peshkov et al. (2012)) as references which we deteriorate in different ways (random addition of boundaries, random removal boundaries, near-miss errors introduction). This provide us with various measures on controlled datasets and with an interesting benchmark for various linguistic segmentation tasks.