Enrique Henestroza Anguiano

Also published as: Enrique Henestroza Anguiano


2012

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Probabilistic Lexical Generalization for French Dependency Parsing
Enrique Henestroza Anguiano | Marie Candito
Proceedings of the ACL 2012 Joint Workshop on Statistical Parsing and Semantic Processing of Morphologically Rich Languages

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Ubiquitous Usage of a Broad Coverage French Corpus: Processing the Est Republicain corpus
Djamé Seddah | Marie Candito | Benoit Crabbé | Enrique Henestroza Anguiano
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

In this paper, we introduce a set of resources that we have derived from the EST RÉPUBLICAIN CORPUS, a large, freely-available collection of regional newspaper articles in French, totaling 150 million words. Our resources are the result of a full NLP treatment of the EST RÉPUBLICAIN CORPUS: handling of multi-word expressions, lemmatization, part-of-speech tagging, and syntactic parsing. Processing of the corpus is carried out using statistical machine-learning approaches - joint model of data driven lemmatization and part- of-speech tagging, PCFG-LA and dependency based models for parsing - that have been shown to achieve state-of-the-art performance when evaluated on the French Treebank. Our derived resources are made freely available, and released according to the original Creative Common license for the EST RÉPUBLICAIN CORPUS. We additionally provide an overview of the use of these resources in various applications, in particular the use of generated word clusters from the corpus to alleviate lexical data sparseness for statistical parsing.

2011

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FreDist : Construction automatique d’un thésaurus distributionnel pour le Français (FreDist : Automatic construction of distributional thesauri for French)
Enrique Henestroza Anguiano | Pascal Denis
Actes de la 18e conférence sur le Traitement Automatique des Langues Naturelles. Articles courts

Dans cet article, nous présentons FreDist, un logiciel libre pour la construction automatique de thésaurus distributionnels à partir de corpus de texte, ainsi qu’une évaluation des différents ressources ainsi produites. Suivant les travaux de (Lin, 1998) et (Curran, 2004), nous utilisons un corpus journalistique de grande taille et implémentons différentes options pour : le type de relation contexte lexical, la fonction de poids, et la fonction de mesure de similarité. Prenant l’EuroWordNet français et le WOLF comme références, notre évaluation révèle, de manière originale, que c’est l’approche qui combine contextes linéaires (ici, de type bigrammes) et contextes syntaxiques qui semble fournir le meilleur thésaurus. Enfin, nous espérons que notre logiciel, distribué avec nos meilleurs thésaurus pour le français, seront utiles à la communauté TAL.

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Parse Correction with Specialized Models for Difficult Attachment Types
Enrique Henestroza Anguiano | Marie Candito
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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A Word Clustering Approach to Domain Adaptation: Effective Parsing of Biomedical Texts
Marie Candito | Enrique Henestroza Anguiano | Djamé Seddah
Proceedings of the 12th International Conference on Parsing Technologies

2010

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Benchmarking of Statistical Dependency Parsers for French
Marie Candito | Joakim Nivre | Pascal Denis | Enrique Henestroza Anguiano
Coling 2010: Posters