Foundations of statistical natural language processing
Foundations of statistical natural language processing
WSD Algorithm Applied to a NLP System
NLDB '00 Proceedings of the 5th International Conference on Applications of Natural Language to Information Systems-Revised Papers
PHORA: A NLP System for Spanish
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Specification Marks for Word Sense Disambiguation: New Development
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Interface for WordNet Enrichment with Classification Systems
DEXA '01 Proceedings of the 12th International Conference on Database and Expert Systems Applications
Word Sense Disambiguation with Specification Marks in Unrestricted Texts
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
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
An empirical study of the domain dependence of supervised word sense disambiguation systems
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
A Web Information Extraction System to DB Prototyping
NLDB '02 Proceedings of the 6th International Conference on Applications of Natural Language to Information Systems-Revised Papers
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This paper presents a method to combine two unsupervised methods (Specification Marks, Conceptual Density) and one supervised (Maximum Entropy) for the automatic resolution of lexical ambiguity of nouns in English texts. The main objective is to improved the accuracy of knowledge-based methods with statistical information supplied by the corpus-based method. We explore a way of combining the classification results of the three methods: "voting" is the way we have chosen to combine the three methods in one unique decision.These three methods have been applied both individually as in a combined way to disambiguate a set of polysemous words. Our results show that a combination of different knowledge-based methods and the addition of statistical information from a corpus-based method might eventually lead to improve accuracy of first ones.