Joan-Andreu Sánchez

Also published as: Joan-Andreu Sanchez, Joan Andreu Sánchez


2018

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On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models
Joan Andreu Sánchez | Martha Alicia Rocha | Verónica Romero | Mauricio Villegas
Computational Linguistics, Volume 44, Issue 1 - April 2018

Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other finite-state models as weighted finite-state automata, word lattices, and hidden Markov models. Therefore, they share many similar properties and problems. Entropy measures of finite-state models have been investigated in the past in order to study the information capacity of these models. The derivational entropy quantifies the uncertainty that the model has about the probability distribution it represents. The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences. The computation of the entropy from a weighted finite-state automaton requires a normalized model. This article studies an efficient computation of the derivational entropy of left-to-right probabilistic finite-state automata, and it introduces an efficient algorithm for normalizing weighted finite-state automata. The efficient computation of the derivational entropy is also extended to continuous hidden Markov models.

2011

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Handwritten Text Recognition for Historical Documents
Verónica Romero | Nicolás Serrano | Alejandro H. Toselli | Joan Andreu Sánchez | Enrique Vidal
Proceedings of the Workshop on Language Technologies for Digital Humanities and Cultural Heritage

2010

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Enlarged Search Space for SITG Parsing
Guillem Gascó | Joan-Andreu Sánchez | José-Miguel Benedí
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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Interactive Predictive Parsing using a Web-based Architecture
Ricardo Sánchez-Sáez | Luis A. Leiva | Joan-Andreu Sánchez | José-Miguel Benedí
Proceedings of the NAACL HLT 2010 Demonstration Session

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UPV-PRHLT English–Spanish System for WMT10
Germán Sanchis-Trilles | Jesús Andrés-Ferrer | Guillem Gascó | Jesús González-Rubio | Pascual Martínez-Gómez | Martha-Alicia Rocha | Joan-Andreu Sánchez | Francisco Casacuberta
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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The UPV-PRHLT Combination System for WMT 2010
Jesús González-Rubio | Germán Sanchis-Trilles | Joan-Andreu Sánchez | Jesús Andrés-Ferrer | Guillem Gascó | Pascual Martínez-Gómez | Martha-Alicia Rocha | Francisco Casacuberta
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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Confidence Measures for Error Discrimination in an Interactive Predictive Parsing Framework
Ricardo Sánchez-Sáez | Joan Andreu Sánchez | José Miguel Benedí
Coling 2010: Posters

2009

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Statistical Confidence Measures for Probabilistic Parsing
Ricardo Sánchez-Sáez | Joan-Andreu Sánchez | José-Miguel Benedí Ruíz
Proceedings of the International Conference RANLP-2009

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Interactive Predictive Parsing
Ricardo Sánchez-Sáez | Joan-Andreu Sánchez | José-Miguel Benedí
Proceedings of the 11th International Conference on Parsing Technologies (IWPT’09)

2008

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Using Parsed Corpora for Estimating Stochastic Inversion Transduction Grammars
Germán Sanchis | Joan Andreu Sánchez
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

An important problem when using Stochastic Inversion Transduction Grammars is their computational cost. More specifically, when dealing with corpora such as Europarl. only one iteration of the estimation algorithm becomes prohibitive. In this work, we apply a reduction of the cost by taking profit of the bracketing information in parsed corpora and show machine translation results obtained with a bracketed Europarl corpus, yielding interresting improvements when increasing the number of non-terminal symbols.

2006

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Stochastic Inversion Transduction Grammars for Obtaining Word Phrases for Phrase-based Statistical Machine Translation
Joan Andreu Sánchez | José Miguel Benedí
Proceedings on the Workshop on Statistical Machine Translation

2000

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Combination of N-Grams and Stochastic Context-Free Grammars for Language Modeling
Jose-Miguel Benedi | Joan-Andreu Sanchez
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics