pwc-archive/papers-with-abstracts huggingface dataset:
Conceptual transfer: Using local classifiers for transfer selection
Authors: Gregor Thurmair
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

A key challenge for Machine Translation is transfer selection, i.e. to find the right translation for a given word from a set of alternatives (1:n). This problem becomes the more important the larger the dictionary is, as the number of alternatives increases. The contribution presents a novel approach for transfer selection, called conceptual transfer, where selection is done using classifiers based on the conceptual context of a translation candidate on the source language side. Such classifiers are built automatically by parallel corpus analysis: Creating subcorpora for each translation of a 1:n package, and identifying correlating concepts in these subcorpora as features of the classifier. The resulting resource can easily be linked to transfer components of MT systems as it does not depend on internal analysis structures. Tests show that conceptual transfer outperforms the selection techniques currently used in operational MT systems.
Metadata as Linked Open Data: mapping disparate XML metadata registries into one RDF/OWL registry.
Authors: Marta Villegas, Maite Melero, N{\'u}ria Bel
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The proliferation of different metadata schemas and models pose serious problems of interoperability. Maintaining isolated repositories with overlapping data is costly in terms of time and effort. In this paper, we describe how we have achieved a Linked Open Data version of metadata descriptions coming from heterogeneous sources, originally encoded in XML. The resulting model is much simpler than the original XSD schema and avoids problems typical of XML syntax, such as semantic ambiguity and order constraint. Moreover, the open world assumption of RDF/OWL allows to naturally integrate objects from different schemas and to add further extensions, facilitating merging of different models as well as linking to external data. Apart from the advantages in terms of interoperability and maintainability, the merged repository enables end-users to query multiple sources using a unified schema and is able to present them with implicit knowledge derived from the linked data. The approach we present here is easily scalable to any number of sources and schemas.
Sharing resources between free/open-source rule-based machine translation systems: Grammatical Framework and Apertium
Authors: Gr{\'e}goire D{\'e}trez, V{\'\i}ctor M. S{\'a}nchez-Cartagena, Aarne Ranta
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, Translation

In this paper, we describe two methods developed for sharing linguistic data between two free and open source rule based machine translation systems: Apertium, a shallow-transfer system; and Grammatical Framework (GF), which performs a deeper syntactic transfer. In the first method, we describe the conversion of lexical data from Apertium to GF, while in the second one we automatically extract Apertium shallow-transfer rules from a GF bilingual grammar. We evaluated the resulting systems in a English-Spanish translation context, and results showed the usefulness of the resource sharing and confirmed the a-priori strong and weak points of the systems involved. {\textbackslash}{\textbackslash}
Annotating Arguments: The NOMAD Collaborative Annotation Tool
Authors: Georgios Petasis
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

The huge amount of the available information in the Web creates the need for effective information extraction systems that are able to produce metadata that satisfy user{'}s information needs. The development of such systems, in the majority of cases, depends on the availability of an appropriately annotated corpus in order to learn or evaluate extraction models. The production of such corpora can be significantly facilitated by annotation tools, which provide user-friendly facilities and enable annotators to annotate documents according to a predefined annotation schema. However, the construction of annotation tools that operate in a distributed environment is a challenging task: the majority of these tools are implemented as Web applications, having to cope with the capabilities offered by browsers. This paper describes the NOMAD collaborative annotation tool, which implements an alternative architecture: it remains a desktop application, fully exploiting the advantages of desktop applications, but provides collaborative annotation through the use of a centralised server for storing both the documents and their metadata, and instance messaging protocols for communicating events among all annotators. The annotation tool is implemented as a component of the Ellogon language engineering platform, exploiting its extensive annotation engine, its cross-platform abilities and its linguistic processing components, if such a need arises. Finally, the NOMAD annotation tool is distributed with an open source license, as part of the Ellogon platform.
Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.
Authors: Diana Maynard, Mark Greenwood
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Opinion Mining, Sarcasm Detection, Sentiment Analysis

Sarcasm is a common phenomenon in social media, and is inherently difficult to analyse, not just automatically but often for humans too. It has an important effect on sentiment, but is usually ignored in social media analysis, because it is considered too tricky to handle. While there exist a few systems which can detect sarcasm, almost no work has been carried out on studying the effect that sarcasm has on sentiment in tweets, and on incorporating this into automatic tools for sentiment analysis. We perform an analysis of the effect of sarcasm scope on the polarity of tweets, and have compiled a number of rules which enable us to improve the accuracy of sentiment analysis when sarcasm is known to be present. We consider in particular the effect of sentiment and sarcasm contained in hashtags, and have developed a hashtag tokeniser for GATE, so that sentiment and sarcasm found within hashtags can be detected more easily. According to our experiments, the hashtag tokenisation achieves 98{\%} Precision, while the sarcasm detection achieved 91{\%} Precision and polarity detection 80{\%}.
A stream computing approach towards scalable NLP
Authors: Xabier Artola, Zuhaitz Beloki, Aitor Soroa
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Coreference Resolution, Decision Making

Computational power needs have grown dramatically in recent years. This is also the case in many language processing tasks, due to overwhelming quantities of textual information that must be processed in a reasonable time frame. This scenario has led to a paradigm shift in the computing architectures and large-scale data processing strategies used in the NLP field. In this paper we describe a series of experiments carried out in the context of the NewsReader project with the goal of analyzing the scaling capabilities of the language processing pipeline used in it. We explore the use of Storm in a new approach for scalable distributed language processing across multiple machines and evaluate its effectiveness and efficiency when processing documents on a medium and large scale. The experiments have shown that there is a big room for improvement regarding language processing performance when adopting parallel architectures, and that we might expect even better results with the use of large clusters with many processing nodes.
ISLEX --- a Multilingual Web Dictionary
Authors: {\TH}{\'o}rd{\'\i}s {\'U}lfarsd{\'o}ttir
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

ISLEX is a multilingual Scandinavian dictionary, with Icelandic as a source language and Danish, Norwegian, Swedish, Faroese and Finnish as target languages. Within ISLEX are in fact contained several independent, bilingual dictionaries. While Faroese and Finnish are still under construction, the other languages were opened to the public on the web in November 2011. The use of the dictionary is free of charge and it has been extremely well received by its users. The result of the project is threefold. Firstly, some long awaited Icelandic-Scandinavian dictionaries have been published on the digital medium. Secondly, the project has been an important experience in Nordic language collaboration by jointly building such a work in six countries simultaneously, by academic institutions in Iceland, Denmark, Norway, Sweden, The Faroe Islands and Finland. Thirdly, the work has resulted in a compilation of structured linguistic data of the Nordic languages. This data is suitable for use in further lexicographic work and in various language technology projects.
Exploiting catenae in a parallel treebank alignment
Authors: Manuela Sanguinetti, Cristina Bosco, Loredana Cupi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Translation

This paper aims to introduce the issues related to the syntactic alignment of a dependency-based multilingual parallel treebank, ParTUT. Our approach to the task starts from a lexical mapping and then attempts to expand it using dependency relations. In developing the system, however, we realized that the only dependency relations between the individual nodes were not sufficient to overcome some translation divergences, or shifts, especially in the absence of a direct lexical mapping and a different syntactic realization. For this purpose, we explored the use of a novel syntactic notion introduced in dependency theoretical framework, i.e. that of catena (Latin for {``}chain{''}), which is intended as a group of words that are continuous with respect to dominance. In relation to the task of aligning parallel dependency structures, catenae can be used to explain and identify those cases of one-to-many or many-to-many correspondences, typical of several translation shifts, that cannot be detected by means of direct word-based mappings or bare syntactic relations. The paper presented here describes the overall structure of the alignment system as it has been currently designed, how catenae are extracted from the parallel resource, and their potential relevance to the completion of tree alignment in ParTUT sentences.
Sublanguage Corpus Analysis Toolkit: A tool for assessing the representativeness and sublanguage characteristics of corpora
Authors: Irina Temnikova, William A. Baumgartner Jr., Negacy D. Hailu, Ivelina Nikolova, Tony McEnery, Adam Kilgarriff, Galia Angelova, K. Bretonnel Cohen
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Articles, Sentence

Sublanguages are varieties of language that form subsets of the general language, typically exhibiting particular types of lexical, semantic, and other restrictions and deviance. SubCAT, the Sublanguage Corpus Analysis Toolkit, assesses the representativeness and closure properties of corpora to analyze the extent to which they are either sublanguages, or representative samples of the general language. The current version of SubCAT contains scripts and applications for assessing lexical closure, morphological closure, sentence type closure, over-represented words, and syntactic deviance. Its operation is illustrated with three case studies concerning scientific journal articles, patents, and clinical records. Materials from two language families are analyzed―English (Germanic), and Bulgarian (Slavic). The software is available at sublanguage.sourceforge.net under a liberal Open Source license.
A Large-Scale Evaluation of Pre-editing Strategies for Improving User-Generated Content Translation
Authors: Violeta Seretan, Pierrette Bouillon, Johanna Gerlach
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Domain Adaptation, Machine Translation, Translation

The user-generated content represents an increasing share of the information available today. To make this type of content instantly accessible in another language, the ACCEPT project focuses on developing pre-editing technologies for correcting the source text in order to increase its translatability. Linguistically-informed pre-editing rules have been developed for English and French for the two domains considered by the project, namely, the technical domain and the healthcare domain. In this paper, we present the evaluation experiments carried out to assess the impact of the proposed pre-editing rules on translation quality. Results from a large-scale evaluation campaign show that pre-editing helps indeed attain a better translation quality for a high proportion of the data, the difference with the number of cases where the adverse effect is observed being statistically significant. The ACCEPT pre-editing technology is freely available online and can be used in any Web-based environment to enhance the translatability of user-generated content so that it reaches a broader audience.
Correcting Errors in a New Gold Standard for Tagging Icelandic Text
Authors: Sigr{\'u}n Helgad{\'o}ttir, Hrafn Loftsson, Eir{\'\i}kur R{\"o}gnvaldsson
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Part-Of-Speech Tagging, POS

In this paper, we describe the correction of PoS tags in a new Icelandic corpus, MIM-GOLD, consisting of about 1 million tokens sampled from the Tagged Icelandic Corpus, M{\'I}M, released in 2013. The goal is to use the corpus, among other things, as a new gold standard for training and testing PoS taggers. The construction of the corpus was first described in 2010 together with preliminary work on error detection and correction. In this paper, we describe further the correction of tags in the corpus. We describe manual correction and a method for semi-automatic error detection and correction. We show that, even after manual correction, the number of tagging errors in the corpus can be reduced significantly by applying our semi-automatic detection and correction method. After the semi-automatic error correction, preliminary evaluation of tagging accuracy shows very low error rates. We hope that the existence of the corpus will make it possible to improve PoS taggers for Icelandic text.
Semi-compositional Method for Synonym Extraction of Multi-Word Terms
Authors: B{\'e}atrice Daille, Amir Hazem
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Question Answering, Text Summarization

Automatic synonyms and semantically related word extraction is a challenging task, useful in many NLP applications such as question answering, search query expansion, text summarization, etc. While different studies addressed the task of word synonym extraction, only a few investigations tackled the problem of acquiring synonyms of multi-word terms (MWT) from specialized corpora. To extract pairs of synonyms of multi-word terms, we propose in this paper an unsupervised semi-compositional method that makes use of distributional semantics and exploit the compositional property shared by most MWT. We show that our method outperforms significantly the state-of-the-art.
TUKE-BNews-SK: Slovak Broadcast News Corpus Construction and Evaluation
Authors: Mat{\'u}{\v{s}} Pleva, Jozef Juh{\'a}r
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This article presents an overview of the existing acoustical corpuses suitable for broadcast news automatic transcription task in the Slovak language. The TUKE-BNews-SK database created in our department was built to support the application development for automatic broadcast news processing and spontaneous speech recognition of the Slovak language. The audio corpus is composed of 479 Slovak TV broadcast news shows from public Slovak television called STV1 or Jednotka containing 265 hours of material and 186 hours of clean transcribed speech (4 hours subset extracted for testing purposes). The recordings were manually transcribed using Transcriber tool modified for Slovak annotators and automatic Slovak spell checking. The corpus design, acquisition, annotation scheme and pronunciation transcription is described together with corpus statistics and tools used. Finally the evaluation procedure using automatic speech recognition is presented on the broadcast news and parliamentary speeches test sets.
The Hungarian Gigaword Corpus
Authors: Csaba Oravecz, Tam{\'a}s V{\'a}radi, B{\'a}lint Sass
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Morphological Analysis

The paper reports on the development of the Hungarian Gigaword Corpus (HGC), an extended new edition of the Hungarian National Corpus, with upgraded and redesigned linguistic annotation and an increased size of 1.5 billion tokens. Issues concerning the standard steps of corpus collection and preparation are discussed with special emphasis on linguistic analysis and annotation due to Hungarian having some challenging characteristics with respect to computational processing. As the HGC is designed to serve as a resource for a wide range of linguistic research as well as for the interested public, a number of issues had to be resolved which were raised by trying to find a balance between the above two application areas. The following main objectives have been defined for the development of the HGC, focusing on the pivotal concept of increase in: - size: extending the corpus to minimum 1 billion words, - quality: using new technology for development and analysis, - coverage and representativity: taking new samples of language use and including further variants (transcribed spoken language data and user generated content (social media) from the internet in particular).
Hindi to English Machine Translation: Using Effective Selection in Multi-Model SMT
Authors: Kunal Sachdeva, Rishabh Srivastava, Sambhav Jain, Dipti Sharma
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Machine Translation, regression, Translation

Recent studies in machine translation support the fact that multi-model systems perform better than the individual models. In this paper, we describe a Hindi to English statistical machine translation system and improve over the baseline using multiple translation models. We have considered phrase based as well as hierarchical models and enhanced over both these baselines using a regression model. The system is trained over textual as well as syntactic features extracted from source and target of the aforementioned translations. Our system shows significant improvement over the baseline systems for both automatic as well as human evaluations. The proposed methodology is quite generic and easily be extended to other language pairs as well.
From Natural Language to Ontology Population in the Cultural Heritage Domain. A Computational Linguistics-based approach.
Authors: Maria Pia di Buono, Mario Monteleone
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Information Retrieval, Management, Natural Language Queries, POS, Sentence, Translation

This paper presents an on-going Natural Language Processing (NLP) research based on Lexicon-Grammar (LG) and aimed at improving knowledge management of Cultural Heritage (CH) domain. We intend to demonstrate how our language formalization technique can be applied for both processing and populating a domain ontology. We also use NLP techniques for text extraction and mining to fill information gaps and improve access to cultural resources. The Linguistic Resources (LRs, i.e. electronic dictionaries) we built can be used in the structuring of effective Knowledge Management Systems (KMSs). In order to apply to Parts of Speech (POS) the classes and properties defined by the Conseil Interational des Musees (CIDOC) Conceptual Reference Model (CRM), we use Finite State Transducers/Automata (FSTs/FSA) and their variables built in the form of graphs. FSTs/FSA are also used for analysing corpora in order to retrieve recursive sentence structures, in which combinatorial and semantic constraints identify properties and denote relationship. Besides, FSTs/FSA are also used to match our electronic dictionary entries (ALUs, or Atomic Linguistic Units) to RDF subject, object and predicate (SKOS Core Vocabulary). This matching of linguistic data to RDF and their translation into SPARQL/SERQL path expressions allows the use ALUs to process natural-language queries.
Experiences with Parallelisation of an Existing NLP Pipeline: Tagging Hansard
Authors: Stephen Wattam, Paul Rayson, Alex, Marc er, Jean Anderson
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This poster describes experiences processing the two-billion-word Hansard corpus using a fairly standard NLP pipeline on a high performance cluster. Herein we report how we were able to parallelise and apply a traditional single-threaded batch-oriented application to a platform that differs greatly from that for which it was originally designed. We start by discussing the tagging toolchain, its specific requirements and properties, and its performance characteristics. This is contrasted with a description of the cluster on which it was to run, and specific limitations are discussed such as the overhead of using SAN-based storage. We then go on to discuss the nature of the Hansard corpus, and describe which properties of this corpus in particular prove challenging for use on the system architecture used. The solution for tagging the corpus is then described, along with performance comparisons against a naive run on commodity hardware. We discuss the gains and benefits of using high-performance machinery rather than relatively cheap commodity hardware. Our poster provides a valuable scenario for large scale NLP pipelines and lessons learnt from the experience.
Named Entity Corpus Construction using Wikipedia and DBpedia Ontology
Authors: Younggyun Hahm, Jungyeul Park, Kyungtae Lim, Youngsik Kim, Dosam Hwang, Key-Sun Choi
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5
Tasks: Entity Disambiguation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

In this paper, we propose a novel method to automatically build a named entity corpus based on the DBpedia ontology. Since most of named entity recognition systems require time and effort consuming annotation tasks as training data. Work on NER has thus for been limited on certain languages like English that are resource-abundant in general. As an alternative, we suggest that the NE corpus generated by our proposed method, can be used as training data. Our approach introduces Wikipedia as a raw text and uses the DBpedia data set for named entity disambiguation. Our method is language-independent and easy to be applied to many different languages where Wikipedia and DBpedia are provided. Throughout the paper, we demonstrate that our NE corpus is of comparable quality even to the manually annotated NE corpus.
A model to generate adaptive multimodal job interviews with a virtual recruiter
Authors: Zoraida Callejas, Brian Ravenet, Magalie Ochs, Catherine Pelachaud
Date: 2014-05-01 00:00:00
Proceeding: LREC 2014 5

This paper presents an adaptive model of multimodal social behavior for embodied conversational agents. The context of this research is the training of youngsters for job interviews in a serious game where the agent plays the role of a virtual recruiter. With the proposed model the agent is able to adapt its social behavior according to the anxiety level of the trainee and a predefined difficulty level of the game. This information is used to select the objective of the system (to challenge or comfort the user), which is achieved by selecting the complexity of the next question posed and the agent{'}s verbal and non-verbal behavior. We have carried out a perceptive study that shows that the multimodal behavior of an agent implementing our model successfully conveys the expected social attitudes.