Foundations of statistical natural language processing
Foundations of statistical natural language processing
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
The interaction of knowledge sources in word sense disambiguation
Computational Linguistics
Simple features for Chinese word sense disambiguation
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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This article describes the implementation of Word Sense Disambiguation system that participated in the SemEval-2007 multilingual Chinese-English lexical sample task. We adopted a supervised learning approach with Maximum Entropy classifier. The features used were neighboring words and their part-of-speech, as well as single words in the context, and other syntactic features based on shallow parsing. In addition, we used word category information of a Chinese thesaurus as features for verb disambiguation. For the task we participated in, we obtained precision of 0.716 in micro-average, which is the best among all participated systems.