Foundations of statistical natural language processing
Foundations of statistical natural language processing
Boosting Applied toe Word Sense Disambiguation
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
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
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
One sense per collocation and genre/topic variations
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Semantic pattern learning through maximum entropy-based WSD technique
ConLL '01 Proceedings of the 2001 workshop on Computational Natural Language Learning - Volume 7
Improving Feature Selection for Maximum Entropy-Based Word Sense Disambiguation
PorTAL '02 Proceedings of the Third International Conference on Advances in Natural Language Processing
Word Sense vs. Word Domain Disambiguation: A Maximum Entropy Approach
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
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Supervised learning on a corpus-based Word Sense Disambiguation (WSD) system uses a previously classified set of linguistic contexts. In order to perform the training of the system, it is usual to define a set of functions that inform of any linguistic feature in each example. It is usual to look for the same kind of information for each word too, at least on words of the same part-of-speech.In this paper, a study of feature selection in a supervised learning method of WSD based on corpus, Maximum Entropy conditional probability models, is presented. For a few words selected from the DSO corpus, the behaviour of several types of features has been analyzed in order to identify their contribution to gains in accuracy and to determine the influence of sense frequency in that corpus. This paper shows that not all words are better disambiguated with the same combination of features. Moreover, an improved definition of features in order to increase efficiency is presented as well.