Browsing by Author "Carreras Pérez, Xavier"
Now showing items 1-20 of 22
-
A latent variable ranking model for content-based retrieval
(Springer, 2012)
Conference report
Restricted access - publisher's policySince 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 ... -
A Proposal for wide-coverage Spanish named entity recognition
(2002-04)
Research report
Open AccessThis paper presents a proposal for wide--coverage Named Entity Recognition for Spanish. First, a linguistic description of the typology of Named Entities is proposed. Following this definition an architecture of sequential ... -
A shortest-path method for arc-factored semantic role labeling
(2014)
Conference lecture
Restricted access - publisher's policyWe introduce a Semantic Role Labeling (SRL) parser that finds semantic roles for a predicate together with the syntactic paths linking predicates and arguments. Our main contribution is to formulate SRL in terms of ... -
An empirical study of semi-supervised structured conditional models for dependency parsing
(2009)
Conference report
Open AccessThis 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 ... -
Boosting trees for anti-spam email filtering (Extended version)
(2001-10)
Research report
Open AccessIn this work, a set of comparative experiments for the problem of automatically filtering unwanted electronic mail messages are performed on two public corpora: PU1 and LingSpam. Several variants of the AdaBoost algorithm ... -
Exploiting diversity of margin-based classifiers
(2003-12)
Research report
Open AccessAn experimental comparison among Support Vector Machines, AdaBoost and a recently proposed model for maximizing the margin with Feed-forward Neural Networks has been made on a real-world classification problem, namely ... -
Exponentiated gradient algorithms for conditional random fields and max-margin Markov networks
(2008-08)
Article
Open AccessLog-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 ... -
Joint arc-factored parsing of syntactic and semantic dependencies
(2013-05)
Article
Restricted access - publisher's policyIn 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 ... -
Learning task-specific bilexical embeddings
(2014)
Conference report
Open AccessWe present a method that learns bilexical operators over distributional representations of words and leverages supervised data for a linguistic relation. The learning algorithm exploits lowrank bilinear forms and induces ... -
Margin maximization with feed-forward neural networks: a comparative study with support vector machines and AdaBoost
(2003-06)
Research report
Open AccessFeed-forward Neural Networks (FNN) and Support Vector Machines (SVM) are two machine learning frameworks developed from very different starting points of view. In this work a new learning model for FNN is proposed such ... -
Non-projective parsing for statistical machine translation
(2009)
Conference report
Open AccessWe 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 ... -
Projective dependency parsing with perceptron
(2010)
Conference report
Open AccessWe 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
(2008)
Conference report
Open AccessWe 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
(2012)
Conference report
Restricted access - publisher's policyIn 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
(2013-10-07)
Article
Restricted access - publisher's policyIn 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
(2014)
Conference report
Open AccessWe 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
(2007)
Conference report
Open AccessThis 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
(Coling 2008 Organizing Committee, 2008)
Conference report
Open AccessWe 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
(Association for Computational Linguistics, 2023)
Conference report
Open AccessPrior 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
(2012)
Conference report
Open AccessFinite-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. ...
