Foundations of statistical natural language processing
Foundations of statistical natural language processing
A Baseline Methodology for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Refined lexicon models for statistical machine translation using a maximum entropy approach
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
A decision tree of bigrams is an accurate predictor of word sense
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Experiments in word domain disambiguation for parallel texts
WWSM '00 Proceedings of the ACL-2000 workshop on Word senses and multi-linguality - Volume 8
Supervised sense tagging using support vector machines
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Combining heterogeneous classifiers for word-sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
The Johns Hopkins SENSEVAL2 system descriptions
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Combining data-driven systems for improving Named Entity Recognition
Data & Knowledge Engineering
Corpus-based semantic role approach in information retrieval
Data & Knowledge Engineering
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Multilingual Question Classification based on surface text features
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
Paraphrase identification on the basis of supervised machine learning techniques
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
The role of verb sense disambiguation in semantic role labeling
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
Combining data-driven systems for improving named entity recognition
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
A study of the influence of pos tagging on WSD
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Fine tuning features and post-processing rules to improve named entity recognition
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
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In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed using the SENSEVAL-2 data for the Spanish lexical sample task. Such analysis shows that instead of training with the same kind of information for all words, each one is more effectively learned using a different set of features. This best-feature-selection is used to build some systems based on different maximum entropy classifiers, and a voting system helped by a knowledge-based method.