Exploració per autor "Carreras Pérez, Xavier"
Ara es mostren els items 12-22 de 22
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Projective dependency parsing with perceptron
Carreras Pérez, Xavier; Surdeanu, Mihai; Màrquez Villodre, Lluís (2010)
Text en actes de congrés
Accés obertWe describe an online learning dependency parser for the CoNLL-X Shared Task, based on the bottom-up projective algorithm of Eisner (2000). We experiment with a large feature set that models: the tokens involved in ... -
Simple semi-supervised dependency parsing
Koo, Terry; Carreras Pérez, Xavier; Collins, Michael (2008)
Text en actes de congrés
Accés obertWe present a simple and effective semisupervised method for training dependency parsers. We focus on the problem of lexical representation, introducing features that incorporate word clusters derived from a large unannotated ... -
Spectral learning in non-deterministic dependency parsing
Luque, Franco M.; Quattoni, Ariadna Julieta; Balle Pigem, Borja de; Carreras Pérez, Xavier (2012)
Text en actes de congrés
Accés restringit per política de l'editorialIn this paper we study spectral learning methods for non-deterministic split head-automata grammars, a powerful hidden-state formalism for dependency parsing. We present a learning algorithm that, like other spectral ... -
Spectral learning of weighted automata: a forward-backward perspective
Balle Pigem, Borja de; Carreras Pérez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta (2013-10-07)
Article
Accés restringit per política de l'editorialIn recent years we have seen the development of efficient provably correct algorithms for learning Weighted Finite Automata (WFA). Most of these algorithms avoid the known hardness results by defining parameters beyond the ... -
Spectral regularization for max-margin sequence tagging
Quattoni, Ariadna Julieta; Balle Pigem, Borja de; Carreras Pérez, Xavier; Globerson, Amir (2014)
Text en actes de congrés
Accés obertWe frame max-margin learning of latent variable structured prediction models as a convex optimization problem, making use of scoring functions computed by input-output observable operator models. This learning problem can ... -
Structured prediction models via the matrix-tree theorem
Koo, Terry; Globerson, Amir; Carreras Pérez, Xavier; Collins, Michael (2007)
Text en actes de congrés
Accés obertThis paper provides an algorithmic framework for learning statistical models involving directed spanning trees, or equivalently non-projective dependency structures. We show how partition functions and marginals for directed ... -
TAG, dynamic programming, and the perceptron for efficient, feature-rich parsing
Carreras Pérez, Xavier; Collins, Michael; Koo, Terry (Coling 2008 Organizing Committee, 2008)
Text en actes de congrés
Accés obertWe describe a parsing approach that makes use of the perceptron algorithm, in conjunction with dynamic programming methods, to recover full constituent-based parse trees. The formalism allows a rich set of parse-tree ... -
Translate first reorder later: leveraging monotonicity in semantic parsing
Cazzaro, Francesco; Locatelli, Davide; Quattoni, Ariadna Julieta; Carreras Pérez, Xavier (Association for Computational Linguistics, 2023)
Text en actes de congrés
Accés obertPrior work in semantic parsing has shown that conventional seq2seq models fail at compositional generalization tasks. This limitation led to a resurgence of methods that model alignments between sentences and their ... -
Unsupervised spectral learning of finite-state transducers
Bailly, Raphaël; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta (2012)
Text en actes de congrés
Accés obertFinite-State Transducers (FST) are a standard tool for modeling paired inputoutput sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. ... -
Unsupervised spectral learning of FSTs
Bailly, Raphaël; Carreras Pérez, Xavier; Quattoni, Ariadna Julieta (2013)
Text en actes de congrés
Accés obertFinite-State Transducers (FST) are a standard tool for modeling paired input output sequences and are used in numerous applications, ranging from computational biology to natural language processing. Recently Balle et al. ... -
Unsupervised spectral learning of WCFG as low-rank matrix completion
Bailly, Raphaël; Carreras Pérez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta (Association for Computational Linguistics, 2013)
Comunicació de congrés
Accés obertWe derive a spectral method for unsupervised learning ofWeighted Context Free Grammars. We frame WCFG induction as finding a Hankel matrix that has low rank and is linearly constrained to represent a function computed by ...