Ara es mostren els items 12-22 de 22

    • Projective dependency parsing with perceptron 

      Carreras Pérez, Xavier; Surdeanu, Mihai; Màrquez Villodre, Lluís (2010)
      Text en actes de congrés
      Accés obert
      We 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 obert
      We 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'editorial
      In 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'editorial
      In 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 obert
      We 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 obert
      This 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 obert
      We 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 obert
      Prior 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 obert
      Finite-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 obert
      Finite-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 obert
      We 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 ...