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  • Unsupervised spectral learning of WCFG as low-rank matrix completion

     Bailly, Raphaël; Carreras Perez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta
    Conference on Empirical Methods in Natural Language Processing
    Presentation's date: 2013-10-19
    Presentation of work at congresses

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    We derive a spectral method for unsupervised learning of Weighted 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 inside-outside recursions. The proposed algorithm picks the grammar that agrees with a sample and is the simplest with respect to the nuclear norm of the Hankel matrix.

  • Unsupervised spectral learning of finite-state transducers

     Bailly, Raphaël; Carreras Perez, Xavier; Quattoni, Ariadna Julieta
    Neural Information Processing Systems Conference
    Presentation's date: 2013-12-09
    Presentation of work at congresses

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    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. (ECML-2011) presented a spectral algorithm for learning FST from samples of aligned input-output sequences. In this paper we address the more realistic, yet challenging setting where the alignments are unknown to the learning algorithm. We frame FST learning as finding a low rank Hankel matrix satisfying constraints derived from observable statistics. Under this formulation, we provide identifiability results for FST distributions. Then, following previous work on rank minimization, we propose a regularized convex relaxation of this objective which is based on minimizing a nuclear norm penalty subject to linear constraints and can be solved efficiently.

  • Joint arc-factored parsing of syntactic and semantic dependencies

     Lluis Martorell, Xavier; Carreras Perez, Xavier; Màrquez Villodre, Lluís
    Transactions of the Association for Computational Linguistics (TACL)
    Date of publication: 2013-05
    Journal article

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    In this paper we introduce a joint arc-factored model for syntactic and semantic dependency parsing. The semantic role labeler predicts the full syntactic paths that connect predicates with their arguments. This process is framed as a linear assignment task, which allows to control some well-formedness constraints. For the syntactic part, we define a standard arc-factored dependency model that predicts the full syntactic tree. Finally, we employ dual decomposition techniques to produce consistent syntactic and predicate-argument structures while searching over a large space of syntactic configurations. In experiments on the CoNLL-2009 English benchmark we observe very competitive results.

    In this paper we introduce a joint arc-factored model for syntactic and semantic dependency parsing. The semantic role labeler predicts the full syntactic paths that connect predicates with their arguments. This process is framed as a linear assignment task, which allows to control some well-formedness constraints. For the syntactic part, we define a standard arc-factored dependency model that predicts the full syntactic tree. Finally, we employ dual decomposition techniques to produce consistent syntactic and predicate-argument structures while searching over a large space of syntactic configurations. In experiments on the CoNLL-2009 English benchmark we observe very competitive results.

  • Spectral learning of weighted automata: a forward-backward perspective

     De Balle Pigem, Borja; Carreras Perez, Xavier; Luque, Franco M.; Quattoni, Ariadna Julieta
    Machine learning
    Date of publication: 2013-10-07
    Journal article

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    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 number of states that can be used to quantify the complexity of learning automata under a particular distribution. One such class of methods are the so-called spectral algorithms that measure learning complexity in terms of the smallest singular value of some Hankel matrix. However, despite their simplicity and wide applicability to real problems, their impact in application domains remains marginal to this date. One of the goals of this paper is to remedy this situation by presenting a derivation of the spectral method for learning WFA that¿without sacrificing rigor and mathematical elegance¿puts emphasis on providing intuitions on the inner workings of the method and does not assume a strong background in formal algebraic methods. In addition, our algorithm overcomes some of the shortcomings of previous work and is able to learn from statistics of substrings. To illustrate the approach we present experiments on a real application of the method to natural language parsing.

  • Local loss optimization in operator models: a new insight into spectral learning

     De Balle Pigem, Borja; Quattoni, Ariadna Julieta; Carreras Perez, Xavier
    International Conference on Machine Learning
    Presentation's date: 2012-06-29
    Presentation of work at congresses

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  • Spectral learning in non-deterministic dependency parsing  awarded activity

     Luque, Franco M.; Quattoni, Ariadna Julieta; De Balle Pigem, Borja; Carreras Perez, Xavier
    European Chapter of the Association for Computational Linguistics
    Presentation's date: 2012-02
    Presentation of work at congresses

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    n 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 methods, is efficient and non-susceptible to local minima. We show how this algorithm can be formulated as a technique for inducing hidden structure from distributions computed by forward-backward recursions. Furthermore, we also present an inside-outside algorithm for the parsing model that runs in cubic time, hence maintaining the standard parsing costs for context-free grammars.

    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 methods, is efficient and non-susceptible to local minima. We show how this algorithm can be formulated as a technique for inducing hidden structure from distributions computed by forward-backward recursions. Furthermore, we also present an inside-outside algorithm for the parsing model that runs in cubic time, hence maintaining the standard parsing costs for context-free grammars.

  • A latent variable ranking model for content-based retrieval

     Quattoni, Ariadna Julieta; Carreras Perez, Xavier; Torralba, Antonio
    European Conference on Information Retrieval
    Presentation of work at congresses

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    Since their introduction, ranking SVM models have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of users of the retrieval system. Most previous ranking models learn either a global combination of elementary similarity functions or a combination defined with respect to a single database item. Instead, we propose a “coarse to fine” ranking model where given a query we first compute a distribution over “coarse” classes and then use the linear combination that has been optimized for queries of that class. These coarse classes are hidden and need to be induced by the training algorithm. We propose a latent variable ranking model that induces both the latent classes and the weights of the linear combination for each class from ranking triplets. Our experiments over two large image datasets and a text retrieval dataset show the advantages of our model over learning a global combination as well as a combination for each test point (i.e. transductive setting). Furthermore, compared to the transductive approach our model has a clear computational advantages since it does not need to be retrained for each test query.

  • Best Paper Award of EACL 2012

     Quattoni, Ariadna Julieta; Luque, Franco M.; De Balle Pigem, Borja; Carreras Perez, Xavier
    Award or recognition

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  • Cross-lingual Knowledge Extraction

     Carreras Perez, Xavier
    Participation in a competitive project

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  • Treeler: open-source structured prediction for natural language processing

     Carreras Perez, Xavier
    Workshop on Applications of Pattern Analysis
    Presentation's date: 2011-10-20
    Presentation of work at congresses

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  • A spectral learning algorithm for finite state transducers

     De Balle Pigem, Borja; Quattoni, Ariadna Julieta; Carreras Perez, Xavier
    European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
    Presentation's date: 2011-09-07
    Presentation of work at congresses

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  • Gradient-based reinforcement learning techniques for underwater robotics behavior learning

     El-Fakdi Sencianes, Andrés
    Defense's date: 2011-03-03
    Universitat de Girona
    Theses

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  • Araknion: inducción de modelos lingüísticos a partir de corpora

     Martí, Maria Antònia; Taulé, Mariona; Rodriguez Hontoria, Horacio; Martínez Barco, Patricio Manuel; Carreras Perez, Xavier
    Procesamiento del lenguaje natural
    Date of publication: 2011
    Journal article

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  • Organización de los congresos ECML/PKDD'10

     Balcazar Navarro, Jose Luis; Carreras Perez, Xavier; Casas Fernandez, Bernardino; Gavaldà Mestre, Ricard; Berral Garcia, Josep Lluis; De Balle Pigem, Borja; Quattoni, Ariadna Julieta; Bifet Figuerol, Albert Carles; Arias Vicente, Marta
    Participation in a competitive project

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  • Multilingual On-Line Translation

     Rodriguez Hontoria, Horacio; Gonzalez Bermudez, Meritxell; España Bonet, Cristina; Farwell, David Loring; Carreras Perez, Xavier; Xambó Descamps, Sebastian; Màrquez Villodre, Lluís; Padró Cirera, Lluís; Saludes Closa, Jordi
    Participation in a competitive project

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    An empirical study of semi-supervised structured conditional models for dependency parsing  Open access

     Suzuki, Jun; Isozaki, Hideki; Carreras Perez, Xavier; Collins, Michael
    Conference on Empirical Methods in Natural Language Processing
    Presentation's date: 2009-08
    Presentation of work at congresses

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    This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semisupervised structured conditional models (SS-SCMs) to the dependency parsing problem, whose framework is originally proposed in (Suzuki and Isozaki, 2008). Moreover, we introduce two extensions related to dependency parsing: The first extension is to combine SS-SCMs with another semi-supervised approach, described in (Koo et al., 2008). The second extension is to apply the approach to secondorder parsing models, such as those described in (Carreras, 2007), using a twostage semi-supervised learning approach. We demonstrate the effectiveness of our proposed methods on dependency parsing experiments using two widely used test collections: the Penn Treebank for English, and the Prague Dependency Treebank for Czech. Our best results on test data in the above datasets achieve 93.79% parent-prediction accuracy for English, and 88.05% for Czech.

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    Non-projective parsing for statistical machine translation  Open access

     Carreras Perez, Xavier; Collins, Michael
    Conference on Empirical Methods in Natural Language Processing
    Presentation's date: 2009-08
    Presentation of work at congresses

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    We describe a novel approach for syntaxbased statistical MT, which builds on a variant of tree adjoining grammar (TAG). Inspired by work in discriminative dependency parsing, the key idea in our approach is to allow highly flexible reordering operations during parsing, in combination with a discriminative model that can condition on rich features of the sourcelanguage string. Experiments on translation from German to English show improvements over phrase-based systems, both in terms of BLEU scores and in human evaluations.

    Postprint (author’s final draft)

  • An efficient projection for L1, infinity regularization

     Quattoni, Ariadna Julieta; Carreras Perez, Xavier; Collins, Michael; Darrell, Trevor
    International Conference on Machine Learning
    Presentation's date: 2009-06
    Presentation of work at congresses

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    Simple semi-supervised dependency parsing  Open access

     Koo, Terry; Carreras Perez, Xavier; Collins, Michael
    Annual Meeting of the Association for Computational Linguistics
    Presentation's date: 2008-06
    Presentation of work at congresses

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    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 corpus. We demonstrate the effectiveness of the approach in a series of dependency parsing experiments on the Penn Treebank and Prague Dependency Treebank, and we show that the cluster-based features yield substantial gains in performance across a wide range of conditions. For example, in the case of English unlabeled second-order parsing, we improve from a baseline accuracy of 92:02% to 93:16%, and in the case of Czech unlabeled second-order parsing, we improve from a baseline accuracy of 86:13% to 87:13%. In addition, we demonstrate that our method also improves performance when small amounts of training data are available, and can roughly halve the amount of supervised data required to reach a desired level of performance.

    Postprint (author’s final draft)

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    TAG, dynamic programming, and the perceptron for efficient, feature-rich parsing  Open access

     Carreras Perez, Xavier; Collins, Michael; Koo, Terry
    Conference on Computational Natural Language Learning
    Presentation's date: 2008-08
    Presentation of work at congresses

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    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 features, including PCFGbased features, bigram and trigram dependency features, and surface features. A severe challenge in applying such an approach to full syntactic parsing is the efficiency of the parsing algorithms involved. We show that efficient training is feasible, using a Tree Adjoining Grammar (TAG) based parsing formalism. A lower-order dependency parsing model is used to restrict the search space of the full model, thereby making it efficient. Experiments on the Penn WSJ treebank show that the model achieves state-of-the-art performance, for both constituent and dependency accuracy.

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    Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks  Open access

     Collins, Michael; Globerson, Amir; Koo, Terry; Carreras Perez, Xavier; Bartlett, Peter
    Journal of machine learning research
    Date of publication: 2008-08
    Journal article

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    Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

  • Semantic role labeling: An introduction to the special issue

     Màrquez Villodre, Lluís; Carreras Perez, Xavier; Litkowski, K C; Stevenson, S
    Computational linguistics
    Date of publication: 2008-06
    Journal article

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  • Experiments with a higher-order projective dependency parser

     Carreras Perez, Xavier
    Conference on Computational Natural Language Learning
    Presentation's date: 2007-07
    Presentation of work at congresses

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    Structured prediction models via the matrix-tree theorem  Open access

     Koo, Terry; Globerson, Amir; Carreras Perez, Xavier; Collins, Michael
    Conference on Empirical Methods in Natural Language Processing
    Presentation's date: 2007-06
    Presentation of work at congresses

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    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 spanning trees can be computed by an adaptation of Kirchhoff’s Matrix-Tree Theorem. To demonstrate an application of the method, we perform experiments which use the algorithm in training both log-linear and max-margin dependency parsers. The new training methods give improvements in accuracy over perceptron-trained models.

    Postprint (author’s final draft)

  • Exponentiated gradient algorithms for log-linear structured prediction

     Globerson, Amir; Koo, Terry; Carreras Perez, Xavier; Collins, Michael
    International Conference on Machine Learning
    Presentation's date: 2007
    Presentation of work at congresses

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  • Combination strategies for semantic role labeling

     Surdeanu, Mihai; Màrquez Villodre, Lluís; Carreras Perez, Xavier; Comas Umbert, Pere Ramon
    Journal of artificial intelligence research
    Date of publication: 2007-05
    Journal article

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    Projective dependency parsing with perceptron  Open access

     Carreras Perez, Xavier; Surdeanu, Mihai; Màrquez Villodre, Lluís
    Conference on Computational Natural Language Learning
    Presentation's date: 2006
    Presentation of work at congresses

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    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 dependencies and their immediate context, the surfacetext distance between tokens, and the syntactic context dominated by each dependency. In experiments, the treatment of multilingual information was totally blind.

    Postprint (author’s final draft)

  • LEARNING AND INFERENCE IN PHRASE RECOGNITION:A FILTERING-RANKING ARCHITECTURE USING PERCEPTRON

     Carreras Perez, Xavier
    Defense's date: 2005-10-28
    Department of Software, Universitat Politècnica de Catalunya
    Theses

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  • Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling

     Màrquez Villodre, Lluís; Carreras Perez, Xavier
    9th Conference on Computational Natural Language Learning
    Presentation of work at congresses

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  • Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling

     Carreras Perez, Xavier
    9th Conference on Computational Natural Language Learning
    Presentation's date: 2005-06-30
    Presentation of work at congresses

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  • Filtering-Ranking Perceptron Learning for partial Parsing

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Castro Rabal, Jorge
    Machine learning
    Date of publication: 2005-10
    Journal article

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  • Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling

     Màrquez Villodre, Lluís; Carreras Perez, Xavier
    Conference on Computational Natural Language Learning
    Presentation of work at congresses

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  • Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling

     Carreras Perez, Xavier
    Conference on Computational Natural Language Learning
    Presentation's date: 2004-05-07
    Presentation of work at congresses

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  • Máquinas de Vectores Soporte

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Romero Merino, Enrique
    Date of publication: 2004-02
    Book chapter

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  • FreeLing; An Open-Source Suite of Language Analyzers

     Carreras Perez, Xavier; Chao, I; Padró Cirera, Lluís; Padro Cirera, Montserrat
    4th International Conference on Languages Resources and Evaluation (LREC 2004)
    Presentation of work at congresses

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  • Exploiting Diversity of Margin-based Classifiers

     Romero Merino, Enrique; Carreras Perez, Xavier; Màrquez Villodre, Lluís
    IEEE International Joint Conference on Neural Networks & Internacional Conference on Fuzzy Systems
    Presentation of work at congresses

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  • Hierarchical Recognition of Propositional Arguments with Perceptrons

     Carreras Perez, Xavier; Màrquez Villodre, Lluís
    Conference on Computational Natural Language Learning
    Presentation of work at congresses

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  • Margin Maximization with Feed-forward Neural Networks: A Comparative Study with SVM and AdaBoost

     Romero Merino, Enrique; Màrquez Villodre, Lluís; Carreras Perez, Xavier
    Neurocomputing
    Date of publication: 2004-03
    Journal article

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  • Learning a Perceptron-Based Named Entity Chunker via Online Recognition Feedback

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Padró Cirera, Lluís
    Conference on Computational Natural Language Learning
    Presentation of work at congresses

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  • Named Entity Recognition For Catalan Using Spanish Resources

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Padró Cirera, Lluís
    10th Conference of the European Chapter of the Association for Computational Linguistics
    Presentation of work at congresses

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  • Low-cost Named Entity Classification for Catalan: Exploiting Multilingual Resources and Unlabeled Data

     Màrquez Villodre, Lluís; de Gispert Ramis, Adrià; Carreras Perez, Xavier; Padró Cirera, Lluís
    1st ACL Workshop on Multilingual and Mixed-language Named Entity Recognition: Combining Statistical and Symbolic Models
    Presentation of work at congresses

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  • Margin Maximization with Feed-forward Neural Networks: A Comparative Study with Support Vector Machines and AdaBoost

     Romero Merino, Enrique; Màrquez Villodre, Lluís; Carreras Perez, Xavier
    Date: 2003-06
    Report

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  • Exploiting Diversity of Margin-based Classifiers

     Romero Merino, Enrique; Carreras Perez, Xavier; Màrquez Villodre, Lluís
    Date: 2003-12
    Report

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  • A Simple Named Entity Extractor Using AdaBoost

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Padró Cirera, Lluís
    Conference on Computational Natural Language Learning
    Presentation of work at congresses

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  • A Proposal for Wide-Coverage Spanish Named Entity Recognition

     Arévalo, M; Carreras Perez, Xavier; Màrquez Villodre, Lluís; Martí, Maria Antònia; Padró Cirera, Lluís; Simón, Mª José
    Procesamiento del lenguaje natural
    Date of publication: 2002-05
    Journal article

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  • A Flexible Distributed Architecture for Natural Language Analyzers

     Carreras Perez, Xavier; Padró Cirera, Lluís
    International Conference on Language Resources and Evaluation
    Presentation of work at congresses

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  • Named Entity Extraction using AdaBoost

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Padró Cirera, Lluís
    Conference on Computational Natural Language Learning
    Presentation of work at congresses

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  • Wide-Coverage Spanish Named Entity Extraction

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Padró Cirera, Lluís
    VIII Conferencia Iberoamericana de Inteligencia Artificial
    Presentation of work at congresses

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  • A Proposal for Wide-Coverage Spanish Named Entity Recognition

     Carreras Perez, Xavier; Màrquez Villodre, Lluís; Padró Cirera, Lluís
    Date: 2002-04
    Report

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  • A Proposal for Wide-Coverage Spanish Named Entity Recognition

     Arévalo, M; Carreras Perez, Xavier; Màrquez Villodre, Lluís; Martí, Maria Antònia; Padró Cirera, Lluís; Simon Olmos, Maria Jose
    XVIII Congreso de la Sociedad Española para el Procesamiento del Lenguaje Natural
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