pwc-archive/papers-with-abstracts huggingface dataset:
HuRIC: a Human Robot Interaction Corpus
Authors: Emanuele Bastianelli, Giuseppe Castellucci, Danilo Croce, Luca Iocchi, Roberto Basili, Daniele Nardi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Abstract Meaning Representation, Domain Adaptation, Open-Domain Question Answering, Question Answering, Semantic Parsing

Recent years show the development of large scale resources (e.g. FrameNet for the Frame Semantics) that supported the definition of several state-of-the-art approaches in Natural Language Processing. However, the reuse of existing resources in heterogeneous domains such as Human Robot Interaction is not straightforward. The generalization offered by many data driven methods is strongly biased by the employed data, whose performance in out-of-domain conditions exhibit large drops. In this paper, we present the Human Robot Interaction Corpus (HuRIC). It is made of audio files paired with their transcriptions referring to commands for a robot, e.g. in a home environment. The recorded sentences are annotated with different kinds of linguistic information, ranging from morphological and syntactic information to rich semantic information, according to the Frame Semantics, to characterize robot actions, and Spatial Semantics, to capture the robot environment. All texts are represented through the Abstract Meaning Representation, to adopt a simple but expressive representation of commands, that can be easily translated into the internal representation of the robot.
On the origin of errors: A fine-grained analysis of MT and PE errors and their relationship
Authors: Joke Daems, Lieve Macken, V, Sonia epitte
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Machine Translation, Translation

In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not enough to know that the output of one system is better than the output of another system. A fine-grained error analysis is needed to provide information on the type and location of errors occurring in MT and the corresponding errors occurring after post-editing (PE). This article reports on a fine-grained translation quality assessment approach which was applied to machine translated-texts and the post-edited versions of these texts, made by student post-editors. By linking each error to the corresponding source text-passage, it is possible to identify passages that were problematic in MT, but not after PE, or passages that were problematic even after PE. This method provides rich data on the origin and impact of errors, which can be used to improve post-editor training as well as machine translation systems. We present the results of a pilot experiment on the post-editing of newspaper articles and highlight the advantages of our approach.
The WaveSurfer Automatic Speech Recognition Plugin
Authors: Giampiero Salvi, Niklas Vanhainen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper presents a plugin that adds automatic speech recognition (ASR) functionality to the WaveSurfer sound manipulation and visualisation program. The plugin allows the user to run continuous speech recognition on spoken utterances, or to align an already available orthographic transcription to the spoken material. The plugin is distributed as free software and is based on free resources, namely the Julius speech recognition engine and a number of freely available ASR resources for different languages. Among these are the acoustic and language models we have created for Swedish using the NST database.
Free English and Czech telephone speech corpus shared under the CC-BY-SA 3.0 license
Authors: Mat{\v{e}}j Korvas, Ond{\v{r}}ej Pl{\'a}tek, Ond{\v{r}}ej Du{\v{s}}ek, Luk{\'a}{\v{s}} {\v{Z}}ilka, Filip Jur{\v{c}}{\'\i}{\v{c}}ek
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Domain Adaptation, speech-recognition, Speech Recognition, Spoken Dialogue Systems

We present a dataset of telephone conversations in English and Czech, developed for training acoustic models for automatic speech recognition (ASR) in spoken dialogue systems (SDSs). The data comprise 45 hours of speech in English and over 18 hours in Czech. Large part of the data, both audio and transcriptions, was collected using crowdsourcing, the rest are transcriptions by hired transcribers. We release the data together with scripts for data pre-processing and building acoustic models using the HTK and Kaldi ASR toolkits. We publish also the trained models described in this paper. The data are released under the CC-BY-SA{\textasciitilde}3.0 license, the scripts are licensed under Apache{\textasciitilde}2.0. In the paper, we report on the methodology of collecting the data, on the size and properties of the data, and on the scripts and their use. We verify the usability of the datasets by training and evaluating acoustic models using the presented data and scripts.
Creating a Gold Standard Corpus for the Extraction of Chemistry-Disease Relations from Patent Texts
Authors: Antje Schlaf, Claudia Bobach, Matthias Irmer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Named Entity Recognition (NER), Relation, Relation Extraction

This paper describes the creation of a gold standard for chemistry-disease relations in patent texts. We start with an automated annotation of named entities of the domains chemistry (e.g. propranolol) and diseases (e.g. hypertension) as well as of related domains like methods and substances. After that, domain-relevant relations between these entities, e.g. propranolol treats hypertension, have been manually annotated. The corpus is intended to be suitable for developing and evaluating relation extraction methods. In addition, we present two reasoning methods of high precision for automatically extending the set of extracted relations. Chain reasoning provides a method to infer and integrate additional, indirectly expressed relations occurring in relation chains. Enumeration reasoning exploits the frequent occurrence of enumerations in patents and automatically derives additional relations. These two methods are applicable both for verifying and extending the manually annotated data as well as for potential improvements of automatic relation extraction.
The SSPNet-Mobile Corpus: Social Signal Processing Over Mobile Phones.
Authors: Anna Polychroniou, Hugues Salamin, Aless Vinciarelli, ro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This article presents the SSPNet-Mobile Corpus, a collection of 60 mobile phone calls between unacquainted individuals (120 subjects). The corpus is designed to support research on non-verbal behavior and it has been manually annotated into conversational topics and behavioral events (laughter, fillers, back-channel, etc.). Furthermore, the corpus includes, for each subject, psychometric questionnaires measuring personality, conflict attitude and interpersonal attraction. Besides presenting the main characteristics of the corpus (scenario, subjects, experimental protocol, sensing approach, psychometric measurements), the paper reviews the main results obtained so far using the data.
Projection-based Annotation of a Polish Dependency Treebank
Authors: Alina Wr{\'o}blewska, Adam Przepi{\'o}rkowski
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: ARC, Dependency Parsing, Machine Translation, Question Answering, Word Alignment

This paper presents an approach of automatic annotation of sentences with dependency structures. The approach builds on the idea of cross-lingual dependency projection. The presented method of acquiring dependency trees involves a weighting factor in the processes of projecting source dependency relations to target sentences and inducing well-formed target dependency trees from sets of projected dependency relations. Using a parallel corpus, source trees are transferred onto equivalent target sentences via an extended set of alignment links. Projected arcs are initially weighted according to the certainty of word alignment links. Then, arc weights are recalculated using a method based on the EM selection algorithm. Maximum spanning trees selected from EM-scored digraphs and labelled with appropriate grammatical functions constitute a target dependency treebank. Extrinsic evaluation shows that parsers trained on such a treebank may perform comparably to parsers trained on a manually developed treebank.
Bootstrapping an Italian VerbNet: data-driven analysis of verb alternations
Authors: Gianluca Lebani, Veronica Viola, Aless Lenci, ro
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Classification, General Classification, Machine Translation, Word Sense Disambiguation

The goal of this paper is to propose a classification of the syntactic alternations admitted by the most frequent Italian verbs. The data-driven two-steps procedure exploited and the structure of the identified classes of alternations are presented in depth and discussed. Even if this classification has been developed with a practical application in mind, namely the semi-automatic building of a VerbNet-like lexicon for Italian verbs, partly following the methodology proposed in the context of the VerbNet project, its availability may have a positive impact on several related research topics and Natural Language Processing tasks
Self-training a Constituency Parser using n-gram Trees
Authors: Arda {\c{C}}elebi, Arzucan {\"O}zg{\"u}r
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Constituency Parsing, Sentence

In this study, we tackle the problem of self-training a feature-rich discriminative constituency parser. We approach the self-training problem with the assumption that while the full sentence parse tree produced by a parser may contain errors, some portions of it are more likely to be correct. We hypothesize that instead of feeding the parser the guessed full sentence parse trees of its own, we can break them down into smaller ones, namely n-gram trees, and perform self-training on them. We build an n-gram parser and transfer the distinct expertise of the {\$}n{\$}-gram parser to the full sentence parser by using the Hierarchical Joint Learning (HJL) approach. The resulting jointly self-trained parser obtains slight improvement over the baseline.
A Tagged Corpus and a Tagger for Urdu
Authors: Bushra Jawaid, Amir Kamran, Ond{\v{r}}ej Bojar
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74{\%} on test data.
Lexical Substitution Dataset for German
Authors: Kostadin Cholakov, Chris Biemann, Judith Eckle-Kohler, Iryna Gurevych
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: LEMMA, Question Answering, Sentence, Text Simplification, Word Sense Disambiguation

This article describes a lexical substitution dataset for German. The whole dataset contains 2,040 sentences from the German Wikipedia, with one target word in each sentence. There are 51 target nouns, 51 adjectives, and 51 verbs randomly selected from 3 frequency groups based on the lemma frequency list of the German WaCKy corpus. 200 sentences have been annotated by 4 professional annotators and the remaining sentences by 1 professional annotator and 5 additional annotators who have been recruited via crowdsourcing. The resulting dataset can be used to evaluate not only lexical substitution systems, but also different sense inventories and word sense disambiguation systems.
A cascade approach for complex-type classification
Authors: Lauren Romeo, Sara Mendes, N{\'u}ria Bel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Classification, General Classification, Information Retrieval, Machine Translation, Question Answering, Vocal Bursts Type Prediction, Word Sense Disambiguation

The work detailed in this paper describes a 2-step cascade approach for the classification of complex-type nominals. We describe an experiment that demonstrates how a cascade approach performs when the task consists in distinguishing nominals from a given complex-type from any other noun in the language. Overall, our classifier successfully identifies very specific and not highly frequent lexical items such as complex-types with high accuracy, and distinguishes them from those instances that are not complex types by using lexico-syntactic patterns indicative of the semantic classes corresponding to each of the individual sense components of the complex type. Although there is still room for improvement with regard to the coverage of the classifiers developed, the cascade approach increases the precision of classification of the complex-type nouns that are covered in the experiment presented.
Generating a Resource for Products and Brandnames Recognition. Application to the Cosmetic Domain.
Authors: C{\'e}dric Lopez, Fr{\'e}d{\'e}rique Segond, Olivier Hondermarck, Paolo Curtoni, Luca Dini
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Named Entity Recognition task needs high-quality and large-scale resources. In this paper, we present RENCO, a based-rules system focused on the recognition of entities in the Cosmetic domain (brandnames, product names, {\^a}¦). RENCO has two main objectives: 1) Generating resources for named entity recognition; 2) Mining new named entities relying on the previous generated resources. In order to build lexical resources for the cosmetic domain, we propose a system based on local lexico-syntactic rules complemented by a learning module. As the outcome of the system, we generate both a simple lexicon and a structured lexicon. Results of the evaluation show that even if RENCO outperforms a classic Conditional Random Fields algorithm, both systems should combine their respective strengths.
Annotation of specialized corpora using a comprehensive entity and relation scheme
Authors: Louise Del{\'e}ger, Anne-Laure Ligozat, Cyril Grouin, Pierre Zweigenbaum, Aur{\'e}lie N{\'e}v{\'e}ol
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Relation

Annotated corpora are essential resources for many applications in Natural Language Processing. They provide insight on the linguistic and semantic characteristics of the genre and domain covered, and can be used for the training and evaluation of automatic tools. In the biomedical domain, annotated corpora of English texts have become available for several genres and subfields. However, very few similar resources are available for languages other than English. In this paper we present an effort to produce a high-quality corpus of clinical documents in French, annotated with a comprehensive scheme of entities and relations. We present the annotation scheme as well as the results of a pilot annotation study covering 35 clinical documents in a variety of subfields and genres. We show that high inter-annotator agreement can be achieved using a complex annotation scheme.
Annotation Pro + TGA: automation of speech timing analysis
Authors: Katarzyna Klessa, Dafydd Gibbon
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper reports on two tools for the automatic statistical analysis of selected properties of speech timing on the basis of speech annotation files. The tools, one online (TGA, Time Group Analyser) and one offline (Annotation Pro+TGA), are intended to support the rapid analysis of speech timing data without the need to create specific scripts or spreadsheet functions for this purpose. The software calculates, inter alia, mean, median, rPVI, nPVI, slope and intercept functions within interpausal groups, provides visualisations of timing patterns, as well as correlations between these, and parses interpausal groups into hierarchies based on duration relations. Although many studies, especially in speech technology, use computational means, enquiries have shown that a large number of phoneticians and phonetics students do not have script creation skills and therefore use traditional copy+spreadsheet techniques, which are slow, preclude the analysis of large data sets, and are prone to inconsistencies. The present tools have been tested in a number of studies on English, Mandarin and Polish, and are introduced here with reference to results from these studies.
Polysemy Index for Nouns: an Experiment on Italian using the PAROLE SIMPLE CLIPS Lexical Database
Authors: Francesca Frontini, Valeria Quochi, Sebastian Pad{\'o}, Monica Monachini, Jason Utt
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Vocal Bursts Type Prediction

An experiment is presented to induce a set of polysemous basic type alternations (such as Animal-Food, or Building-Institution) by deriving them from the sense alternations found in an existing lexical resource. The paper builds on previous work and applies those results to the Italian lexicon PAROLE SIMPLE CLIPS. The new results show how the set of frequent type alternations that can be induced from the lexicon is partly different from the set of polysemy relations selected and explicitely applied by lexicographers when building it. The analysis of mismatches shows that frequent type alternations do not always correpond to prototypical polysemy relations, nevertheless the proposed methodology represents a useful tool offered to lexicographers to systematically check for possible gaps in their resource.
YouDACC: the Youtube Dialectal Arabic Comment Corpus
Authors: Ahmed Salama, Houda Bouamor, Behrang Mohit, Kemal Oflazer
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper presents YOUDACC, an automatically annotated large-scale multi-dialectal Arabic corpus collected from user comments on Youtube videos. Our corpus covers different groups of dialects: Egyptian (EG), Gulf (GU), Iraqi (IQ), Maghrebi (MG) and Levantine (LV). We perform an empirical analysis on the crawled corpus and demonstrate that our location-based proposed method is effective for the task of dialect labeling.
Towards an Encyclopedia of Compositional Semantics: Documenting the Interface of the English Resource Grammar
Authors: Dan Flickinger, Emily M. Bender, Stephan Oepen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Named Entity Recognition (NER)

We motivate and describe the design and development of an emerging encyclopedia of compositional semantics, pursuing three objectives. We first seek to compile a comprehensive catalogue of interoperable semantic analyses, i.e., a precise characterization of meaning representations for a broad range of common semantic phenomena. Second, we operationalize the discovery of semantic phenomena and their definition in terms of what we call their semantic fingerprint, a formal account of the building blocks of meaning representation involved and their configuration. Third, we ground our work in a carefully constructed semantic test suite of minimal exemplars for each phenomenon, along with a `target{'} fingerprint that enables automated regression testing. We work towards these objectives by codifying and documenting the body of knowledge that has been constructed in a long-term collaborative effort, the development of the LinGO English Resource Grammar. Documentation of its semantic interface is a prerequisite to use by non-experts of the grammar and the analyses it produces, but this effort also advances our own understanding of relevant interactions among phenomena, as well as of areas for future work in the grammar.
Enriching the ``Senso Comune'' Platform with Automatically Acquired Data
Authors: Tommaso Caselli, Laure Vieu, Carlo Strapparava, Guido Vetere
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper reports on research activities on automatic methods for the enrichment of the Senso Comune platform. At this stage of development, we will report on two tasks, namely word sense alignment with MultiWordNet and automatic acquisition of Verb Shallow Frames from sense annotated data in the MultiSemCor corpus. The results obtained are satisfying. We achieved a final F-measure of 0.64 for noun sense alignment and a F-measure of 0.47 for verb sense alignment, and an accuracy of 68{\textbackslash}{\%} on the acquisition of Verb Shallow Frames.
Online experiments with the Percy software framework - experiences and some early results
Authors: Christoph Draxler
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

In early 2012 the online perception experiment software Percy was deployed on a production server at our lab. Since then, 38 experiments have been made publicly available, with a total of 3078 experiment sessions. In the course of time, the software has been continuously updated and extended to adapt to changing user requirements. Web-based editors for the structure and layout of the experiments have been developed. This paper describes the system architecture, presents usage statistics, discusses typical characteristics of online experiments, and gives an outlook on ongoing work. webapp.phonetik.uni-muenchen.de/WebExperiment lists all currently active experiments.
Improving the exploitation of linguistic annotations in ELAN
Authors: Onno Crasborn, Han Sloetjes
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Language Identification

This paper discusses some improvements in recent and planned versions of the multimodal annotation tool ELAN, which are targeted at improving the usability of annotated files. Increased support for multilingual documents is provided, by allowing for multilingual vocabularies and by specifying a language per document, annotation layer (tier) or annotation. In addition, improvements in the search possibilities and the display of the results have been implemented, which are especially relevant in the interpretation of the results of complex multi-tier searches.
A Deep Context Grammatical Model For Authorship Attribution
Authors: Simon Fuller, Phil Maguire, Philippe Moser
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Authorship Attribution, model, Sentence

We define a variable-order Markov model, representing a Probabilistic Context Free Grammar, built from the sentence-level, de-lexicalized parse of source texts generated by a standard lexicalized parser, which we apply to the authorship attribution task. First, we motivate this model in the context of previous research on syntactic features in the area, outlining some of the general strengths and limitations of the overall approach. Next we describe the procedure for building syntactic models for each author based on training cases. We then outline the attribution process - assigning authorship to the model which yields the highest probability for the given test case. We demonstrate the efficacy for authorship attribution over different Markov orders and compare it against syntactic features trained by a linear kernel SVM. We find that the model performs somewhat less successfully than the SVM over similar features. In the conclusion, we outline how we plan to employ the model for syntactic evaluation of literary texts.