38 research outputs found

    Constructing a Large-Scale English-Persian Parallel Corpus

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    In recent years the exploitation of large text corpora in solving various kinds of linguistic problems, including those of translation, is commonplace. Yet a large-scale English-Persian corpus is still unavailable, because of certain difficulties and the amount of work required to overcome them.The project reported here is an attempt to constitute an English-Persian parallel corpus composed of digital texts and Web documents containing little or no noise. The Internet is useful because translations of existing texts are often published on the Web. The task is to find parallel pages in English and Persian, to judge their translation quality, and to download and align them. The corpus so created is of course open; that is, more material can be added as the need arises.One of the main activities associated with building such a corpus is to develop software for parallel concordancing, in which a user can enter a search string in one language and see all the citations for that string in it and corresponding sentences in the target language. Our intention is to construct general translation memory software using the present English-Persian parallel corpus.Au cours des dernières années, l’exploitation de grands corpus de textes pour résoudre des problèmes linguistiques, notamment des problèmes de traduction, est devenue une pratique courante. Jusqu’à récemment, aucun corpus bilingue anglais-persan à grande échelle n’avait été constitué, en raison des difficultés qu’implique une telle entreprise.Cet article présente un projet réalisé en vue de colliger des corpus de textes numériques variés, tels que des documents du réseau Internet, avec le moins de bruit possible. L’utilisation d’Internet peut être considérée comme une aide précieuse car, souvent, il existe des traductions antérieures qui sont déjà publiées sur le Web. La tâche consiste à trouver les pages parallèles en anglais et en persan, à évaluer la qualité de leur traduction, à les télécharger et à les aligner. Le corpus ainsi obtenu est un corpus ouvert, soit un corpus auquel de nouvelles données peuvent être ajoutées, selon les besoins.Une des principales conséquences de l’élaboration d’un tel corpus est la mise au point d’un logiciel de concordance parallèle, dans lequel l’utilisateur pourrait introduire une chaîne de caractères dans une langue et afficher toutes les citations concernant cette chaîne dans la langue recherchée ainsi que des phrases correspondantes dans la langue cible. L’étape suivante serait d’utiliser ce corpus parallèle pour construire un logiciel de traduction générale.Le corpus bilingue aligné se trouve être utile dans beaucoup d’autres cas, entre autres pour la traduction par ordinateur, pour lever les ambiguïtés de sens, pour le rétablissement des données interlangues, en lexicographie ainsi que pour l’apprentissage des langues

    EuroWordNet: final report

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    EuroWordNet: final report

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    Systematisierung und Evaluierung von Clustering-Verfahren im Information Retrieval

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    Im Rahmen der vorliegenden Diplomarbeit werden Verfahren zur Clusteranalyse sowie deren Anwendungsmöglichkeiten zur Optimierung der Rechercheergebnisse von Information Retrievalsystemen untersucht. Die Grundlage der vergleichenden Evaluation erfolgversprechender Ansätze zur Clusteranalyse anhand der Domain Specific Monolingual Tasks des Cross-Language Evaluation Forums 2006 bildet die systematische Analyse der in der Forschung etablierten Verfahren zur Clusteranalyse. Die Implementierung ausgewählter Clusterverfahren wird innerhalb eines bestehenden, Lucene-basierten Retrievalsystems durchgeführt. Zusätzlich wird dieses System im Rahmen dieser Arbeit mit Komponenten zur Query Expansion und zur Datenfusion ausgestattet. Diese beiden Ansätze haben sich in der Forschung zur automatischen Optimierung von Retrievalergebnissen durchgesetzt und bilden daher die Bewertungsgrundlage für die implementierten Konzepte zur Optimierung von Rechercheergebnissen auf Basis der Clusteranalyse. Im Ergebnis erweist sich das lokale Dokument Clustering auf Basis des k-means Clustering-Algorithmus in Kombination mit dem Pseudo-Relevanz-Feedback Ansatz zur Selektion der Dokumente für die Query Expansion als besonders erfolgversprechend. Darüber hinaus wird gezeigt, dass mit Hilfe der Datenfusion auf Basis des Z-Score Operators die Ergebnisse verschiedener Indizierungsverfahren so kombiniert werden können, dass sehr gute und insbesondere sehr robuste Rechercheergebnisse erreicht werden.Within the present diploma thesis, widely used Cluster Analysis approaches are studied in respect to their application to optimize the results of Information Retrieval systems. A systematic analysis of approved methods of the Cluster Analysis is the basis of the comparative evaluation of promising approaches to use the Cluster Analysis to optimize retrieval results. The evaluation is accomplished by the participation at the Domain Specific Monolingual Tasks of the Cross-Language Evaluation Forum 2006. The implementation of selected approaches for Clustering is realized within the framework of an existing Lucene-based retrieval system. Within the scope of work, this system will be supplemented with components for Query Expansion and Data Fusion. Both approaches have prevailed in the research of automatic optimization of retrieval results. Therefore, they are the basis of assessment of the implemented methods, which aim at improving the results of retrieval and are based on Cluster Analysis. The results show that selecting documents for Query Expansion with the help of local Document Clustering based on the k-means Clustering algorithm combined with the Blind Feedback approach is very promising. Furthermore, the Data Fusion approach based on the Z-Score operator proves to be very useful to combine retrieval results of different indexing methods. In fact, this approach achieves very good and in particular very robust results of retrieval

    Factoid question answering for spoken documents

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    In this dissertation, we present a factoid question answering system, specifically tailored for Question Answering (QA) on spoken documents. This work explores, for the first time, which techniques can be robustly adapted from the usual QA on written documents to the more difficult spoken documents scenario. More specifically, we study new information retrieval (IR) techniques designed for speech, and utilize several levels of linguistic information for the speech-based QA task. These include named-entity detection with phonetic information, syntactic parsing applied to speech transcripts, and the use of coreference resolution. Our approach is largely based on supervised machine learning techniques, with special focus on the answer extraction step, and makes little use of handcrafted knowledge. Consequently, it should be easily adaptable to other domains and languages. In the work resulting of this Thesis, we have impulsed and coordinated the creation of an evaluation framework for the task of QA on spoken documents. The framework, named QAst, provides multi-lingual corpora, evaluation questions, and answers key. These corpora have been used in the QAst evaluation that was held in the CLEF workshop for the years 2007, 2008 and 2009, thus helping the developing of state-of-the-art techniques for this particular topic. The presentend QA system and all its modules are extensively evaluated on the European Parliament Plenary Sessions English corpus composed of manual transcripts and automatic transcripts obtained by three different Automatic Speech Recognition (ASR) systems that exhibit significantly different word error rates. This data belongs to the CLEF 2009 track for QA on speech transcripts. The main results confirm that syntactic information is very useful for learning to rank question candidates, improving results on both manual and automatic transcripts unless the ASR quality is very low. Overall, the performance of our system is comparable or better than the state-of-the-art on this corpus, confirming the validity of our approach.En aquesta Tesi, presentem un sistema de Question Answering (QA) factual, especialment ajustat per treballar amb documents orals. En el desenvolupament explorem, per primera vegada, quines tècniques de les habitualment emprades en QA per documents escrit són suficientment robustes per funcionar en l'escenari més difícil de documents orals. Amb més especificitat, estudiem nous mètodes de Information Retrieval (IR) dissenyats per tractar amb la veu, i utilitzem diversos nivells d'informació linqüística. Entre aquests s'inclouen, a saber: detecció de Named Entities utilitzant informació fonètica, "parsing" sintàctic aplicat a transcripcions de veu, i també l'ús d'un sub-sistema de detecció i resolució de la correferència. La nostra aproximació al problema es recolza en gran part en tècniques supervisades de Machine Learning, estant aquestes enfocades especialment cap a la part d'extracció de la resposta, i fa servir la menor quantitat possible de coneixement creat per humans. En conseqüència, tot el procés de QA pot ser adaptat a altres dominis o altres llengües amb relativa facilitat. Un dels resultats addicionals de la feina darrere d'aquesta Tesis ha estat que hem impulsat i coordinat la creació d'un marc d'avaluació de la taska de QA en documents orals. Aquest marc de treball, anomenat QAst (Question Answering on Speech Transcripts), proporciona un corpus de documents orals multi-lingüe, uns conjunts de preguntes d'avaluació, i les respostes correctes d'aquestes. Aquestes dades han estat utilitzades en les evaluacionis QAst que han tingut lloc en el si de les conferències CLEF en els anys 2007, 2008 i 2009; d'aquesta manera s'ha promogut i ajudat a la creació d'un estat-de-l'art de tècniques adreçades a aquest problema en particular. El sistema de QA que presentem i tots els seus particulars sumbòduls, han estat avaluats extensivament utilitzant el corpus EPPS (transcripcions de les Sessions Plenaries del Parlament Europeu) en anglès, que cónté transcripcions manuals de tots els discursos i també transcripcions automàtiques obtingudes mitjançant tres reconeixedors automàtics de la parla (ASR) diferents. Els reconeixedors tenen característiques i resultats diferents que permetes una avaluació quantitativa i qualitativa de la tasca. Aquestes dades pertanyen a l'avaluació QAst del 2009. Els resultats principals de la nostra feina confirmen que la informació sintàctica és mol útil per aprendre automàticament a valorar la plausibilitat de les respostes candidates, millorant els resultats previs tan en transcripcions manuals com transcripcions automàtiques, descomptat que la qualitat de l'ASR sigui molt baixa. En general, el rendiment del nostre sistema és comparable o millor que els altres sistemes pertanyents a l'estat-del'art, confirmant així la validesa de la nostra aproximació

    Feasibility of using citations as document summaries

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    The purpose of this research is to establish whether it is feasible to use citations as document summaries. People are good at creating and selecting summaries and are generally the standard for evaluating computer generated summaries. Citations can be characterized as concept symbols or short summaries of the document they are citing. Similarity metrics have been used in retrieval and text summarization to determine how alike two documents are. Similarity metrics have never been compared to what human subjects think are similar between two documents. If similarity metrics reflect human judgment, then we can mechanize the selection of citations that act as short summaries of the document they are citing. The research approach was to gather rater data comparing document abstracts to citations about the same document and then to statistically compare those results to several document metrics; frequency count, similarity metric, citation location and type of citation. There were two groups of raters, subject experts and non-experts. Both groups of raters were asked to evaluate seven parameters between abstract and citations: purpose, subject matter, methods, conclusions, findings, implications, readability, andunderstandability. The rater was to identify how strongly the citation represented the content of the abstract, on a five point likert scale. Document metrics were collected for frequency count, cosine, and similarity metric between abstracts and associated citations. In addition, data was collected on the location of the citations and the type of citation. Location was identified and dummy coded for introduction, method, discussion, review of the literature and conclusion. Citations were categorized and dummy coded for whether they refuted, noted, supported, reviewed, or applied information about the cited document. The results show there is a relationship between some similarity metrics and human judgment of similarity.Ph.D., Information Studies -- Drexel University, 200

    Nodalida 2005 - proceedings of the 15th NODALIDA conference

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    Statistical approaches for natural language modelling and monotone statistical machine translation

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    Esta tesis reune algunas contribuciones al reconocimiento de formas estadístico y, más especícamente, a varias tareas del procesamiento del lenguaje natural. Varias técnicas estadísticas bien conocidas se revisan en esta tesis, a saber: estimación paramétrica, diseño de la función de pérdida y modelado estadístico. Estas técnicas se aplican a varias tareas del procesamiento del lenguajes natural tales como clasicación de documentos, modelado del lenguaje natural y traducción automática estadística. En relación con la estimación paramétrica, abordamos el problema del suavizado proponiendo una nueva técnica de estimación por máxima verosimilitud con dominio restringido (CDMLEa ). La técnica CDMLE evita la necesidad de la etapa de suavizado que propicia la pérdida de las propiedades del estimador máximo verosímil. Esta técnica se aplica a clasicación de documentos mediante el clasificador Naive Bayes. Más tarde, la técnica CDMLE se extiende a la estimación por máxima verosimilitud por leaving-one-out aplicandola al suavizado de modelos de lenguaje. Los resultados obtenidos en varias tareas de modelado del lenguaje natural, muestran una mejora en términos de perplejidad. En a la función de pérdida, se estudia cuidadosamente el diseño de funciones de pérdida diferentes a la 0-1. El estudio se centra en aquellas funciones de pérdida que reteniendo una complejidad de decodificación similar a la función 0-1, proporcionan una mayor flexibilidad. Analizamos y presentamos varias funciones de pérdida en varias tareas de traducción automática y con varios modelos de traducción. También, analizamos algunas reglas de traducción que destacan por causas prácticas tales como la regla de traducción directa; y, así mismo, profundizamos en la comprensión de los modelos log-lineares, que son de hecho, casos particulares de funciones de pérdida. Finalmente, se proponen varios modelos de traducción monótonos basados en técnicas de modelado estadístico .Andrés Ferrer, J. (2010). Statistical approaches for natural language modelling and monotone statistical machine translation [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7109Palanci

    Feature Ranking for Text Classifiers

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    Feature selection based on feature ranking has received much attention by researchers in the field of text classification. The major reasons are their scalability, ease of use, and fast computation. %, However, compared to the search-based feature selection methods such as wrappers and filters, they suffer from poor performance. This is linked to their major deficiencies, including: (i) feature ranking is problem-dependent; (ii) they ignore term dependencies, including redundancies and correlation; and (iii) they usually fail in unbalanced data. While using feature ranking methods for dimensionality reduction, we should be aware of these drawbacks, which arise from the function of feature ranking methods. In this thesis, a set of solutions is proposed to handle the drawbacks of feature ranking and boost their performance. First, an evaluation framework called feature meta-ranking is proposed to evaluate ranking measures. The framework is based on a newly proposed Differential Filter Level Performance (DFLP) measure. It was proved that, in ideal cases, the performance of text classifier is a monotonic, non-decreasing function of the number of features. Then we theoretically and empirically validate the effectiveness of DFLP as a meta-ranking measure to evaluate and compare feature ranking methods. The meta-ranking framework is also examined by a stopword extraction problem. We use the framework to select appropriate feature ranking measure for building domain-specific stoplists. The proposed framework is evaluated by SVM and Rocchio text classifiers on six benchmark data. The meta-ranking method suggests that in searching for a proper feature ranking measure, the backward feature ranking is as important as the forward one. Second, we show that the destructive effect of term redundancy gets worse as we decrease the feature ranking threshold. It implies that for aggressive feature selection, an effective redundancy reduction should be performed as well as feature ranking. An algorithm based on extracting term dependency links using an information theoretic inclusion index is proposed to detect and handle term dependencies. The dependency links are visualized by a tree structure called a term dependency tree. By grouping the nodes of the tree into two categories, including hub and link nodes, a heuristic algorithm is proposed to handle the term dependencies by merging or removing the link nodes. The proposed method of redundancy reduction is evaluated by SVM and Rocchio classifiers for four benchmark data sets. According to the results, redundancy reduction is more effective on weak classifiers since they are more sensitive to term redundancies. It also suggests that in those feature ranking methods which compact the information in a small number of features, aggressive feature selection is not recommended. Finally, to deal with class imbalance in feature level using ranking methods, a local feature ranking scheme called reverse discrimination approach is proposed. The proposed method is applied to a highly unbalanced social network discovery problem. In this case study, the problem of learning a social network is translated into a text classification problem using newly proposed actor and relationship modeling. Since social networks are usually sparse structures, the corresponding text classifiers become highly unbalanced. Experimental assessment of the reverse discrimination approach validates the effectiveness of the local feature ranking method to improve the classifier performance when dealing with unbalanced data. The application itself suggests a new approach to learn social structures from textual data
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