pwc-archive/papers-with-abstracts huggingface dataset:
Creating and Curating a Cross-Language Person-Entity Linking Collection
Authors: Dawn Lawrie, James Mayfield, Paul McNamee, Douglas Oard
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Entity Linking, Knowledge Base Population, Named Entity Recognition (NER)

To stimulate research in cross-language entity linking, we present a new test collection for evaluating the accuracy of cross-language entity linking in twenty-one languages. This paper describes an efficient way to create and curate such a collection, judiciously exploiting existing language resources. Queries are created by semi-automatically identifying person names on the English side of a parallel corpus, using judgments obtained through crowdsourcing to identify the entity corresponding to the name, and projecting the English name onto the non-English document using word alignments. Name projections are then curated, again through crowdsourcing. This technique resulted in the first publicly available multilingual cross-language entity linking collection. The collection includes approximately 55,000 queries, comprising between 875 and 4,329 queries for each of twenty-one non-English languages.
The TARSQI Toolkit
Authors: Marc Verhagen, James Pustejovsky
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Question Answering

We present and demonstrate the updated version of the TARSQI Toolkit, a suite of temporal processing modules that extract temporal information from natural language texts. It parses the document and identifies temporal expressions, recognizes events, anchor events to temporal expressions and orders events relative to each other. The toolkit was previously demonstrated at COLING 2008, but has since seen substantial changes including: (1) incorporation of a new time expression tagger, (2){\textasciitilde}embracement of stand-off annotation, (3) application to the medical domain and (4) introduction of narrative containers.
A corpus of general and specific sentences from news
Authors: Annie Louis, Ani Nenkova
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Articles, General Classification, Information Retrieval, Question Answering, Sentence

We present a corpus of sentences from news articles that are annotated as general or specific. We employed annotators on Amazon Mechanical Turk to mark sentences from three kinds of news articlesâ€•reports on events, finance news and science journalism. We introduce the resulting corpus, with focus on annotator agreement, proportion of general/specific sentences in the articles and results for automatic classification of the two sentence types.
Annotation Trees: LDC's customizable, extensible, scalable, annotation infrastructure
Authors: Jonathan Wright, Kira Griffitt, Joe Ellis, Stephanie Strassel, Brendan Callahan
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Reading Comprehension

In recent months, LDC has developed a web-based annotation infrastructure centered around a tree model of annotations and a Ruby on Rails application called the LDC User Interface (LUI). The effort aims to centralize all annotation into this single platform, which means annotation is always available remotely, with no more software required than a web browser. While the design is monolithic in the sense of handling any number of annotation projects, it is also scalable, as it is distributed over many physical and virtual machines. Furthermore, minimizing customization was a core design principle, and new functionality can be plugged in without writing a full application. The creation and customization of GUIs is itself done through the web interface, without writing code, with the aim of eventually allowing project managers to create a new task without developer intervention. Many of the desirable features follow from the model of annotations as trees, and the operationalization of annotation as tree modification.
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing
Authors: Elena Filatova
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Opinion Mining, Sarcasm Detection, Sentence, Sentiment Analysis

The ability to reliably identify sarcasm and irony in text can improve the performance of many Natural Language Processing (NLP) systems including summarization, sentiment analysis, etc. The existing sarcasm detection systems have focused on identifying sarcasm on a sentence level or for a specific phrase. However, often it is impossible to identify a sentence containing sarcasm without knowing the context. In this paper we describe a corpus generation experiment where we collect regular and sarcastic Amazon product reviews. We perform qualitative and quantitative analysis of the corpus. The resulting corpus can be used for identifying sarcasm on two levels: a document and a text utterance (where a text utterance can be as short as a sentence and as long as a whole document).
YADAC: Yet another Dialectal Arabic Corpus
Authors: Rania Al-Sabbagh, Roxana Girju
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Chunking, Dialect Identification, Part-Of-Speech Tagging, POS, POS Tagging, Sentiment Analysis

This paper presents the first phase of building YADAC ― a multi-genre Dialectal Arabic (DA) corpus ― that is compiled using Web data from microblogs (i.e. Twitter), blogs/forums and online knowledge market services in which both questions and answers are user-generated. In addition to introducing two new genres to the current efforts of building DA corpora (i.e. microblogs and question-answer pairs extracted from online knowledge market services), the paper highlights and tackles several new issues related to building DA corpora that have not been handled in previous studies: function-based Web harvesting and dialect identification, vowel-based spelling variation, linguistic hypercorrection and its effect on spelling variation, unsupervised Part-of-Speech (POS) tagging and base phrase chunking for DA. Although the algorithms for both POS tagging and base-phrase chunking are still under development, the results are promising.
An NLP Curator (or: How I Learned to Stop Worrying and Love NLP Pipelines)
Authors: James Clarke, Vivek Srikumar, Mark Sammons, Dan Roth
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Management

Natural Language Processing continues to grow in popularity in a range of research and commercial applications, yet managing the wide array of potential NLP components remains a difficult problem. This paper describes Curator, an NLP management framework designed to address some common problems and inefficiencies associated with building NLP process pipelines; and Edison, an NLP data structure library in Java that provides streamlined interactions with Curator and offers a range of useful supporting functionality.
Hindi Subjective Lexicon: A Lexical Resource for Hindi Adjective Polarity Classification
Authors: Akshat Bakliwal, Piyush Arora, Vasudeva Varma
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: General Classification, Sentiment Analysis

With recent developments in web technologies, percentage web content in Hindi is growing up at a lighting speed. This information can prove to be very useful for researchers, governments and organization to learn what's on public mind, to make sound decisions. In this paper, we present a graph based wordnet expansion method to generate a full (adjective and adverb) subjective lexicon. We used synonym and antonym relations to expand the initial seed lexicon. We show three different evaluation strategies to validate the lexicon. We achieve 70.4{\%} agreement with human annotators and {\^a}¼79{\%} accuracy on product review classification. Main contribution of our work 1) Developing a lexicon of adjectives and adverbs with polarity scores using Hindi Wordnet. 2) Developing an annotated corpora of Hindi Product Reviews.
Annotating Near-Identity from Coreference Disagreements
Authors: Marta Recasens, M. Ant{\`o}nia Mart{\'\i}, Constantin Orasan
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

We present an extension of the coreference annotation in the English NP4E and the Catalan AnCora-CA corpora with near-identity relations, which are borderline cases of coreference. The annotated subcorpora have 50K tokens each. Near-identity relations, as presented by Recasens et al. (2010; 2011), build upon the idea that identity is a continuum rather than an either/or relation, thus introducing a middle ground category to explain currently problematic cases. The first annotation effort that we describe shows that it is not possible to annotate near-identity explicitly because subjects are not fully aware of it. Therefore, our second annotation effort used an indirect method, and arrived at near-identity annotations by inference from the disagreements between five annotators who had only a two-alternative choice between coreference and non-coreference. The results show that whereas as little as 2-6{\%} of the relations were explicitly annotated as near-identity in the former effort, up to 12-16{\%} of the relations turned out to be near-identical following the indirect method of the latter effort.
The REX corpora: A collection of multimodal corpora of referring expressions in collaborative problem solving dialogues
Authors: Takenobu Tokunaga, Ryu Iida, Asuka Terai, Naoko Kuriyama
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper describes a collection of multimodal corpora of referring expressions, the REX corpora. The corpora have two notable features, namely (1) they include time-aligned extra-linguistic information such as participant actions and eye-gaze on top of linguistic information, (2) dialogues were collected with various configurations in terms of the puzzle type, hinting and language. After describing how the corpora were constructed and sketching out each, we present an analysis of various statistics for the corpora with respect to the various configurations mentioned above. The analysis showed that the corpora have different characteristics in the number of utterances and referring expressions in a dialogue, the task completion time and the attributes used in the referring expressions. In this respect, we succeeded in constructing a collection of corpora that included a variety of characteristics by changing the configurations for each set of dialogues, as originally planned. The corpora are now under preparation for publication, to be used for research on human reference behaviour.
Statistical Machine Translation without Source-side Parallel Corpus Using Word Lattice and Phrase Extension
Authors: Takanori Kusumoto, Tomoyosi Akiba
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Machine Translation, Translation

Statistical machine translation (SMT) requires a parallel corpus between the source and target languages. Although a pivot-translation approach can be applied to a language pair that does not have a parallel corpus directly between them, it requires both source―pivot and pivot―target parallel corpora. We propose a novel approach to apply SMT to a resource-limited source language that has no parallel corpus but has only a word dictionary for the pivot language. The problems with dictionary-based translations lie in their ambiguity and incompleteness. The proposed method uses a word lattice representation of the pivot-language candidates and word lattice decoding to deal with the ambiguity; the lattice expansion is accomplished by using a pivot―target phrase translation table to compensate for the incompleteness. Our experimental evaluation showed that this approach is promising for applying SMT, even when a source-side parallel corpus is lacking.
Knowledge-Rich Context Extraction and Ranking with KnowPipe
Authors: Anne-Kathrin Schumann
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

This paper presents ongoing Phd thesis work dealing with the extraction of knowledge-rich contexts from text corpora for terminographic purposes. Although notable progress in the field has been made over recent years, there is yet no methodology or integrated workflow that is able to deal with multiple, typologically different languages and different domains, and that can be handled by non-expert users. Moreover, while a lot of work has been carried out to research the KRC extraction step, the selection and further analysis of results still involves considerable manual work. In this view, the aim of this paper is two-fold. Firstly, the paper presents a ranking algorithm geared at supporting the selection of high-quality contexts once the extraction has been finished and describes ranking experiments with Russian context candidates. Secondly, it presents the KnowPipe framework for context extraction: KnowPipe aims at providing a processing environment that allows users to extract knowledge-rich contexts from text corpora in different languages using shallow and deep processing techniques. In its current state of development, KnowPipe provides facilities for preprocessing Russian and German text corpora, for pattern-based knowledge-rich context extraction from these corpora using shallow analysis as well as tools for ranking Russian context candidates.
Brand Pitt: A Corpus to Explore the Art of Naming
Authors: G{\"o}zde {\"O}zbal, Carlo Strapparava, Marco Guerini
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5

The name of a company or a brand is the key element to a successful business. A good name is able to state the area of competition and communicate the promise given to customers by evoking semantic associations. Although various resources provide distinct tips for inventing creative names, little research was carried out to investigate the linguistic aspects behind the naming mechanism. Besides, there might be latent methods that copywriters unconsciously use. In this paper, we describe the annotation task that we have conducted on a dataset of creative names collected from various resources to create a gold standard for linguistic creativity in naming. Based on the annotations, we compile common and latent methods of naming and explore the correlations among linguistic devices, provoked effects and business domains. This resource represents a starting point for a corpus based approach to explore the art of naming.
Evaluating automatic cross-domain Dutch semantic role annotation
Authors: Orph{\'e}e De Clercq, Veronique Hoste, Paola Monachesi
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Coreference Resolution, Diversity, Semantic Role Labeling

In this paper we present the first corpus where one million Dutch words from a variety of text genres have been annotated with semantic roles. 500K have been completely manually verified and used as training material to automatically label another 500K. All data has been annotated following an adapted version of the PropBank guidelines. The corpus's rich text type diversity and the availability of manually verified syntactic dependency structures allowed us to experiment with an existing semantic role labeler for Dutch. In order to test the system's portability across various domains, we experimented with training on individual domains and compared this with training on multiple domains by adding more data. Our results show that training on large data sets is necessary but that including genre-specific training material is also crucial to optimize classification. We observed that a small amount of in-domain training data is already sufficient to improve our semantic role labeler.
Syntactic annotation of spontaneous speech: application to call-center conversation data
Authors: Thierry Bazillon, Melanie Deplano, Frederic Bechet, Alexis Nasr, Benoit Favre
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Dependency Parsing, POS, Speech Recognition, Spoken Language Understanding

This paper describes the syntactic annotation process of the DECODA corpus. This corpus contains manual transcriptions of spoken conversations recorded in the French call-center of the Paris Public Transport Authority (RATP). Three levels of syntactic annotation have been performed with a semi-supervised approach: POS tags, Syntactic Chunks and Dependency parses. The main idea is to use off-the-shelf NLP tools and models, originaly developped and trained on written text, to perform a first automatic annotation on the manually transcribed corpus. At the same time a fully manual annotation process is performed on a subset of the original corpus, called the GOLD corpus. An iterative process is then applied, consisting in manually correcting errors found in the automatic annotations, retraining the linguistic models of the NLP tools on this corrected corpus, then checking the quality of the adapted models on the fully manual annotations of the GOLD corpus. This process iterates until a certain error rate is reached. This paper describes this process, the main issues raising when adapting NLP tools to process speech transcriptions, and presents the first evaluations performed with these new adapted tools.
Korean Children's Spoken English Corpus and an Analysis of its Pronunciation Variability
Authors: Hyejin Hong, Sunhee Kim, Minhwa Chung
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Recognition

This paper introduces a corpus of Korean-accented English speech produced by children (the Korean Children's Spoken English Corpus: the KC-SEC), which is constructed by Seoul National University. The KC-SEC was developed in support of research and development of CALL systems for Korean learners of English, especially for elementary school learners. It consists of read-speech produced by 96 Korean learners aged from 9 to 12. Overall corpus size is 11,937 sentences, which amount to about 16 hours of speech. Furthermore, a statistical analysis of pronunciation variability appearing in the corpus is performed in order to investigate the characteristics of the Korean children's spoken English. The realized phonemes (hypothesis) are extracted through time-based phoneme alignment, and are compared to the targeted phonemes (reference). The results of the analysis show that: i) the pronunciation variations found frequently in Korean children's speech are devoicing and changing of articulation place or/and manner; and ii) they largely correspond to those of general Korean learners' speech presented in previous studies, despite some differences.
DECODA: a call-centre human-human spoken conversation corpus
Authors: Frederic Bechet, Benjamin Maza, Nicolas Bigouroux, Thierry Bazillon, Marc El-B{\`e}ze, Renato de Mori, Eric Arbillot
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Recognition

The goal of the DECODA project is to reduce the development cost of Speech Analytics systems by reducing the need for manual annotat ion. This project aims to propose robust speech data mining tools in the framework of call-center monitoring and evaluation, by means of weakl y supervised methods. The applicative framework of the project is the call-center of the RATP (Paris public transport authority). This project tackles two very important open issues in the development of speech mining methods from spontaneous speech recorded in call-centers : robus tness (how to extract relevant information from very noisy and spontaneous speech messages) and weak supervision (how to reduce the annotation effort needed to train and adapt recognition and classification models). This paper describes the DECODA corpus collected at the RATP during the project. We present the different annotation levels performed on the corpus, the methods used to obtain them, as well as some evaluation o f the quality of the annotations produced.
The Trilingual ALLEGRA Corpus: Presentation and Possible Use for Lexicon Induction
Authors: Yves Scherrer, Bruno Cartoni
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Sentence

In this paper, we present a trilingual parallel corpus for German, Italian and Romansh, a Swiss minority language spoken in the canton of Grisons. The corpus called ALLEGRA contains press releases automatically gathered from the website of the cantonal administration of Grisons. Texts have been preprocessed and aligned with a current state-of-the-art sentence aligner. The corpus is one of the first of its kind, and can be of great interest, particularly for the creation of natural language processing resources and tools for Romansh. We illustrate the use of such a trilingual resource for automatic induction of bilingual lexicons, which is a real challenge for under-represented languages. We induce a bilingual lexicon for German-Romansh by phrase alignment and evaluate the resulting entries with the help of a reference lexicon. We then show that the use of the third language of the corpus ― Italian ― as a pivot language can improve the precision of the induced lexicon, without loss in terms of quality of the extracted pairs.
Intelligibility assessment in forensic applications
Authors: Giovanni Costantini, Andrea Paoloni, Massimiliano Todisco
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Speech Enhancement

In the context of forensic phonetics the transcription of intercepted signals is particularly important. However, these signals are often degraded and the transcript may not reflect what was actually pronounced. In the absence of the original signal, the only way to see the level of accuracy that can be obtained in the transcription of poor recordings is to develop an objective methodology for intelligibility measurements. This study has been carried out on a corpus specially built to simulate the real conditions of forensic signals. With reference to this corpus a measurement system of intelligibility based on STI (Speech Transmission Index) has been evaluated so as to assess its performance. The result of the experiment shows a high correlation between objective measurements and subjective evaluations. Therefore it is recommended to use the proposed methodology in order to establish whether a given intercepted signal can be transcribed with sufficient reliability.
From medical language processing to BioNLP domain
Authors: Gabriella Pardelli, Manuela Sassi, Sara Goggi, Stefania Biagioni
Date: 2012-05-01 00:00:00
Proceeding: LREC 2012 5
Tasks: Information Retrieval

This paper presents the results of a terminological work on a reference corpus in the domain of Biomedicine. In particular, the research tends to analyse the use of certain terms in Biomedicine in order to verify their change over the time with the aim of retrieving from the net the very essence of documentation. The terminological sample contains words used in BioNLP and biomedicine and identifies which terms are passing from scientific publications to the daily press and which are rather reserved to scientific production. The final scope of this work is to determine how scientific dissemination to an ever larger part of the society enables a public of common citizens to approach communication on biomedical research and development; and its main source is a reference corpus made up of three main repositories from which information related to BioNLP and Biomedicine is extracted. The paper is divided in three sections: 1) an introduction dedicated to data extracted from scientific documentation; 2) the second section devoted to methodology and data description; 3) the third part containing a statistical representation of terms extracted from the archive: indexes and concordances allow to reflect on the use of certain terms in this field and give possible keys for having access to the extraction of knowledge in the digital era.