pwc-archive/papers-with-abstracts huggingface dataset:
Mapping WordNet to the Kyoto ontology
Authors: Egoitz Laparra, German Rigau, Piek Vossen
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Word Sense Disambiguation

This paper describes the connection of WordNet to a generic ontology based on DOLCE. We developed a complete set of heuristics for mapping all WordNet nouns, verbs and adjectives to the ontology. Moreover, the mapping also allows to represent predicates in a uniform and interoperable way, regardless of the way they are expressed in the text and in which language. Together with the ontology, the WordNet mappings provide a extremely rich and powerful basis for semantic processing of text in any domain. In particular, the mapping has been used in a knowledge-rich event-mining system developed for the Asian-European project KYOTO.
Tools for plWordNet Development. Presentation and Perspectives
Authors: Bartosz Broda, Marek Maziarz, Maciej Piasecki
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Question Answering

Building a wordnet is a serious undertaking. Fortunately, Language Technology (LT) can improve the process of wordnet construction both in terms of quality and cost. In this paper we present LT tools used during the construction of plWordNet and their influence on the lexicographer's work-flow. LT is employed in plWordNet development on every possible step: from data gathering through data analysis to data presentation. Nevertheless, every decision requires input from the lexicographer, but the quality of supporting tools is an important factor. Thus a limited evaluation of usefulness of employed tools is carried out on the basis of questionnaires.
Creating a Data Collection for Evaluating Rich Speech Retrieval
Authors: Maria Eskevich, Gareth J. F. Jones, Martha Larson, Roel Ordelman
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Benchmarking, Retrieval

We describe the development of a test collection for the investigation of speech retrieval beyond identification of relevant content. This collection focuses on satisfying user information needs for queries associated with specific types of speech acts. The collection is based on an archive of the Internet video from Internet video sharing platform (blip.tv), and was provided by the MediaEval benchmarking initiative. A crowdsourcing approach was used to identify segments in the video data which contain speech acts, to create a description of the video containing the act and to generate search queries designed to refind this speech act. We describe and reflect on our experiences with crowdsourcing this test collection using the Amazon Mechanical Turk platform. We highlight the challenges of constructing this dataset, including the selection of the data source, design of the crowdsouring task and the specification of queries and relevant items.
Ontologies of Linguistic Annotation: Survey and perspectives
Authors: Christian Chiarcos
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Survey

This paper announces the release of the Ontologies of Linguistic Annotation (OLiA). The OLiA ontologies represent a repository of annotation terminology for various linguistic phenomena on a great band-width of languages. This paper summarizes the results of five years of research, it describes recent developments and directions for further research.
The Open Linguistics Working Group
Authors: Christian Chiarcos, Sebastian Hellmann, Sebastian Nordhoff, Steven Moran, Richard Littauer, Judith Eckle-Kohler, Iryna Gurevych, Silvana Hartmann, Michael Matuschek, Christian M. Meyer
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper describes the Open Linguistics Working Group (OWLG) of the Open Knowledge Foundation (OKFN). The OWLG is an initiative concerned with linguistic data by scholars from diverse fields, including linguistics, NLP, and information science. The primary goal of the working group is to promote the idea of open linguistic resources, to develop means for their representation and to encourage the exchange of ideas across different disciplines. This paper summarizes the progress of the working group, goals that have been identified, problems that we are going to address, and recent activities and ongoing developments. Here, we put particular emphasis on the development of a Linked Open Data (sub-)cloud of linguistic resources that is currently being pursued by several OWLG members.
An Annotation Scheme for Quantifier Scope Disambiguation
Authors: Mehdi Manshadi, James Allen, Mary Swift
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Semantic Parsing, Sentence

Annotating natural language sentences with quantifier scoping has proved to be very hard. In order to overcome the challenge, previous work on building scope-annotated corpora has focused on sentences with two explicitly quantified noun phrases (NPs). Furthermore, it does not address the annotation of scopal operators or complex NPs such as plurals and definites. We present the first annotation scheme for quantifier scope disambiguation where there is no restriction on the type or the number of scope-bearing elements in the sentence. We discuss some of the most prominent complex scope phenomena encountered in annotating the corpus, such as plurality and type-token distinction, and present mechanisms to handle those phenomena.
A generic formalism to represent linguistic corpora in RDF and OWL/DL
Authors: Christian Chiarcos
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper describes POWLA, a generic formalism to represent linguistic corpora by means of RDF and OWL/DL. Unlike earlier approaches in this direction, POWLA is not tied to a specific selection of annotation layers, but rather, it is designed to support any kind of text-oriented annotation. POWLA inherits its generic character from the underlying data model PAULA (Dipper, 2005; Chiarcos et al., 2009) that is based on early sketches of the ISO TC37/SC4 Linguistic Annotation Framework (Ide and Romary, 2004). As opposed to existing standoff XML linearizations for such generic data models, it uses RDF as representation formalism and OWL/DL for validation. The paper discusses advantages of this approach, in particular with respect to interoperability and queriability, which are illustrated for the MASC corpus, an open multi-layer corpus of American English (Ide et al., 2008).
LDC Language Resource Database: Building a Bibliographic Database
Authors: Eleftheria Ahtaridis, Christopher Cieri, Denise DiPersio
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Machine Translation, Speaker Recognition

The Linguistic Data Consortium (LDC) creates and provides language resources (LRs) including data, tools and specifications. In order to assess the impact of these LRs and to support both LR users and authors, LDC is collecting metadata about and URLs for research papers that introduce, describe, critique, extend or rely upon LDC LRs. Current collection efforts focus on papers published in journals and conference proceedings that are available online. To date, nearly 300, or over half of the LRs LDC distributes have been searched for extensively and almost 8000 research papers about these LRs have been documented. This paper discusses the issues with collecting references and includes preliminary analysis of those results. The remaining goals of the project are also outlined.
A hierarchical approach with feature selection for emotion recognition from speech
Authors: Panagiotis Giannoulis, Gerasimos Potamianos
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Classification, Dimensionality Reduction, Emotion Classification, Emotion Recognition, feature selection, General Classification, Speech Recognition

We examine speaker independent emotion classification from speech, reporting experiments on the Berlin database across six basic emotions. Our approach is novel in a number of ways: First, it is hierarchical, motivated by our belief that the most suitable feature set for classification is different for each pair of emotions. Further, it uses a large number of feature sets of different types, such as prosodic, spectral, glottal flow based, and AM-FM ones. Finally, it employs a two-stage feature selection strategy to achieve discriminative dimensionality reduction. The approach results to a classification rate of 85{\%}, comparable to the state-of-the-art on this dataset.
A Concise Query Language with Search and Transform Operations for Corpora with Multiple Levels of Annotation
Authors: Anil Kumar Singh
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Chunking, Part-Of-Speech Tagging

The usefulness of annotated corpora is greatly increased if there is an associated tool that can allow various kinds of operations to be performed in a simple way. Different kinds of annotation frameworks and many query languages for them have been proposed, including some to deal with multiple layers of annotation. We present here an easy to learn query language for a particular kind of annotation framework based on Â‘threaded trees', which are somewhere between the complete order of a tree and the anarchy of a graph. Through 'typed' threads, they can allow multiple levels of annotation in the same document. Our language has a simple, intuitive and concise syntax and high expressive power. It allows not only to search for complicated patterns with short queries but also allows data manipulation and specification of arbitrary return values. Many of the commonly used tasks that otherwise require writing programs, can be performed with one or more queries. We compare the language with some others and try to evaluate it.
A Study of Word-Classing for MT Reordering
Authors: Ananthakrishnan Ramanathan, Karthik Visweswariah
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dependency Parsing, Language Modelling, Machine Translation, POS, TAG

MT systems typically use parsers to help reorder constituents. However most languages do not have adequate treebank data to learn good parsers, and such training data is extremely time-consuming to annotate. Our earlier work has shown that a reordering model learned from word-alignments using POS tags as features can improve MT performance (Visweswariah et al., 2011). In this paper, we investigate the effect of word-classing on reordering performance using this model. We show that unsupervised word clusters perform somewhat worse but still reasonably well, compared to a part-of-speech (POS) tagger built with a small amount of annotated data; while a richer tag set including case and gender-number-person further improves reordering performance by around 1.2 monolingual BLEU points. While annotating this richer tagset is more complicated than annotating the base tagset, it is much easier than annotating treebank data.
Large aligned treebanks for syntax-based machine translation
Authors: Gideon Kotz{\'e}, V, Vincent eghinste, Scott Martens, J{\"o}rg Tiedemann
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Language Modelling, Machine Translation, Translation

We present a collection of parallel treebanks that have been automatically aligned on both the terminal and the nonterminal constituent level for use in syntax-based machine translation. We describe how they were constructed and applied to a syntax- and example-based machine translation system called Parse and Corpus-Based Machine Translation (PaCo-MT). For the language pair Dutch to English, we present evaluation scores of both the nonterminal constituent alignments and the MT system itself, and in the latter case, compare them with those of Moses, a current state-of-the-art statistical MT system, when trained on the same data.
Collecting and Using Comparable Corpora for Statistical Machine Translation
Authors: Inguna Skadi{\c{n}}a, Ahmet Aker, Nikos Mastropavlos, Fangzhong Su, Dan Tufis, Mateja Verlic, Andrejs Vasi{\c{l}}jevs, Bogdan Babych, Paul Clough, Robert Gaizauskas, Nikos Glaros, Monica Lestari Paramita, M{\=a}rcis Pinnis
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation

Lack of sufficient parallel data for many languages and domains is currently one of the major obstacles to further advancement of automated translation. The ACCURAT project is addressing this issue by researching methods how to improve machine translation systems by using comparable corpora. In this paper we present tools and techniques developed in the ACCURAT project that allow additional data needed for statistical machine translation to be extracted from comparable corpora. We present methods and tools for acquisition of comparable corpora from the Web and other sources, for evaluation of the comparability of collected corpora, for multi-level alignment of comparable corpora and for extraction of lexical and terminological data for machine translation. Finally, we present initial evaluation results on the utility of collected corpora in domain-adapted machine translation and real-life applications.
Recognition of Polish Derivational Relations Based on Supervised Learning Scheme
Authors: Maciej Piasecki, Radoslaw Ramocki, Marek Maziarz
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

The paper presents construction of {\textbackslash}emph{Derywator} -- a language tool for the recognition of Polish derivational relations. It was built on the basis of machine learning in a way following the bootstrapping approach: a limited set of derivational pairs described manually by linguists in plWordNet is used to train {\textbackslash}emph{Derivator}. The tool is intended to be applied in semi-automated expansion of plWordNet with new instances of derivational relations. The training process is based on the construction of two transducers working in the opposite directions: one for prefixes and one for suffixes. Internal stem alternations are recognised, recorded in a form of mapping sequences and stored together with transducers. Raw results produced by {\textbackslash}emph{Derivator} undergo next corpus-based and morphological filtering. A set of derivational relations defined in plWordNet is presented. Results of tests for different derivational relations are discussed. A problem of the necessary corpus-based semantic filtering is analysed. The presented tool depends to a very little extent on the hand-crafted knowledge for a particular language, namely only a table of possible alternations and morphological filtering rules must be exchanged and it should not take longer than a couple of working days.
Combining Formal Concept Analysis and semantic information for building ontological structures from texts : an exploratory study
Authors: S{\'\i}lvia Moraes, Vera Lima
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This work studies conceptual structures based on the Formal Concept Analysis method. We build these structures based on lexico-semantic information extracted from texts, among which we highlight the semantic roles. In our research, we propose ways to include semantic roles in concepts produced by this formal method. We analyze the contribution of semantic roles and verb classes in the composition of these concepts through structural measures. In these studies, we use the Penn Treebank Sample and SemLink 1.1 corpora, both in English.
Diversifiable Bootstrapping for Acquiring High-Coverage Paraphrase Resource
Authors: Hideki Shima, Teruko Mitamura
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Machine Translation, Relation Extraction, Text Summarization, Vocal Bursts Intensity Prediction

Recognizing similar or close meaning on different surface form is a common challenge in various Natural Language Processing and Information Access applications. However, we identified multiple limitations in existing resources that can be used for solving the vocabulary mismatch problem. To this end, we will propose the Diversifiable Bootstrapping algorithm that can learn paraphrase patterns with a high lexical coverage. The algorithm works in a lightly-supervised iterative fashion, where instance and pattern acquisition are interleaved, each using information provided by the other. By tweaking a parameter in the algorithm, resulting patterns can be diversifiable with a specific degree one can control.
The Netlog Corpus. A Resource for the Study of Flemish Dutch Internet Language
Authors: Mike Kestemont, Claudia Peersman, Benny De Decker, Guy De Pauw, Kim Luyckx, Roser Morante, Frederik Vaassen, Janneke van de Loo, Walter Daelemans
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Lemmatization, POS, POS Tagging, Semantic Role Labeling

Although in recent years numerous forms of Internet communication ― such as e-mail, blogs, chat rooms and social network environments ― have emerged, balanced corpora of Internet speech with trustworthy meta-information (e.g. age and gender) or linguistic annotations are still limited. In this paper we present a large corpus of Flemish Dutch chat posts that were collected from the Belgian online social network Netlog. For all of these posts we also acquired the users' profile information, making this corpus a unique resource for computational and sociolinguistic research. However, for analyzing such a corpus on a large scale, NLP tools are required for e.g. automatic POS tagging or lemmatization. Because many NLP tools fail to correctly analyze the surface forms of chat language usage, we propose to normalize this anomalous' input into a format suitable for existing NLP solutions for standard Dutch. Additionally, we have annotated a substantial part of the corpus (i.e. the Chatty subset) to provide a gold standard for the evaluation of future approaches to automatic (Flemish) chat language normalization.
Clause-based Discourse Segmentation of Arabic Texts
Authors: Isk Keskes, ar, Farah Benamara, lamia hadrich belguith
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, Discourse Segmentation, Question Answering, Text Summarization

This paper describes a rule-based approach to segment Arabic texts into clauses. Our method relies on an extensive analysis of a large set of lexical cues as well as punctuation marks. Our analysis was carried out on two different corpus genres: news articles and elementary school textbooks. We propose a three steps segmentation algorithm: first by using only punctuation marks, then by relying only on lexical cues and finally by using both typology and lexical cues. The results were compared with manual segmentations elaborated by experts.
Building Japanese Predicate-argument Structure Corpus using Lexical Conceptual Structure
Authors: Yuichiroh Matsubayashi, Yusuke Miyao, Akiko Aizawa
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Natural Language Inference, Semantic Role Labeling

This paper introduces our study on creating a Japanese corpus that is annotated using semantically-motivated predicate-argument structures. We propose an annotation framework based on Lexical Conceptual Structure (LCS), where semantic roles of arguments are represented through a semantic structure decomposed by several primitive predicates. As a first stage of the project, we extended Jackendoff 's LCS theory to increase generality of expression and coverage for verbs frequently appearing in the corpus, and successfully created LCS structures for 60 frequent Japanese predicates in Kyoto university Text Corpus (KTC). In this paper, we report our framework for creating the corpus and the current status of creating an LCS dictionary for Japanese predicates.
An Examination of Cross-Cultural Similarities and Differences from Social Media Data with respect to Language Use
Authors: Mohammad Fazleh Elahi, Paola Monachesi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Cultural Vocal Bursts Intensity Prediction

We present a methodology for analyzing cross-cultural similarities and differences using language as a medium, love as domain, social media as a data source and 'Terms' and 'Topics' as cultural features. We discuss the techniques necessary for the creation of the social data corpus from which emotion terms have been extracted using NLP techniques. Topics of love discussion were then extracted from the corpus by means of Latent Dirichlet Allocation (LDA). Finally, on the basis of these features, a cross-cultural comparison was carried out. For the purpose of cross-cultural analysis, the experimental focus was on comparing data from a culture from the East (India) with a culture from the West (United States of America). Similarities and differences between these cultures have been analyzed with respect to the usage of emotions, their intensities and the topics used during love discussion in social media.
Domain-specific vs. Uniform Modeling for Coreference Resolution
Authors: Olga Uryupina, Massimo Poesio
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: coreference-resolution, Coreference Resolution, Domain Adaptation, Information Retrieval

Several corpora annotated for coreference have been made available in the past decade. These resources differ with respect to their size and the underlying structure: the number of domains and their similarity. Our study compares domain-specific models, learned from small heterogeneous subsets of the investigated corpora, against uniform models, that utilize all the available data. We show that for knowledge-poor baseline systems, domain-specific and uniform modeling yield same results. Systems, relying on large amounts of linguistic knowledge, however, exhibit differences in their performance: with all the designed features in use, domain-specific models suffer from over-fitting, whereas with pre-selected feature sets they tend to outperform union models.
Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text
Authors: Alex Balahur, ra, Jes{\'u}s M. Hermida
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Emotion Classification, Sentiment Analysis

Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Although much research has been performed in this area, most existing approaches are based on word-level analysis of texts and are mostly able to detect only explicit expressions of sentiment. However, in many cases, emotions are not expressed by using words with an affective meaning (e.g. happy), but by describing real-life situations, which readers (based on their commonsense knowledge) detect as being related to a specific emotion. Given the challenges of detecting emotions from contexts in which no lexical clue is present, in this article we present a comparative analysis between the performance of well-established methods for emotion detection (supervised and lexical knowledge-based) and a method we extend, which is based on commonsense knowledge stored in the EmotiNet knowledge base. Our extensive comparative evaluations show that, in the context of this task, the approach based on EmotiNet is the most appropriate.
Extending the adverbial coverage of a French morphological lexicon
Authors: Elsa Tolone, Stavroula Voyatzi, Claude Martineau, Matthieu Constant
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval, Prepositional Phrase Attachment

We present an extension of the adverbial entries of the French morphological lexicon DELA (Dictionnaires Electroniques du LADL / LADL electronic dictionaries). Adverbs were extracted from LGLex, a NLP-oriented syntactic resource for French, which in its turn contains all adverbs extracted from the Lexicon-Grammar tables of both simple adverbs ending in -ment (i.e., '-ly') and compound adverbs. This work exploits fine-grained linguistic information provided in existing resources. The resulting resource is reviewed in order to delete duplicates and is freely available under the LGPL-LR license.