Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
Two graph-based algorithms for state-of-the-art WSD
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Semeval-2007 task 02: evaluating word sense induction and discrimination systems
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
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Traditionally, word sense disambiguation (WSD) involves a different context classification model for each individual word. This paper presents a weakly supervised learning approach to WSD based on learning a word independent context pair classification model. Statistical models are not trained for classifying the word contexts, but for classifying a pair of contexts, i.e. determining if a pair of contexts of the same ambiguous word refers to the same or different senses. Using this approach, annotated corpus of a target word A can be explored to disambiguate senses of a different word B. Hence, only a limited amount of existing annotated corpus is required in order to disambiguate the entire vocabulary. In this research, maximum entropy modeling is used to train the word independent context pair classification model. Then based on the context pair classification results, clustering is performed on word mentions extracted from a large raw corpus. The resulting context clusters are mapped onto the external thesaurus WordNet. This approach shows great flexibility to efficiently integrate heterogeneous knowledge sources, e.g. trigger words and parsing structures. Based on Senseval-3 Lexical Sample standards, this approach achieves state-of-the-art performance in the unsupervised learning category, and performs comparably with the supervised Naïve Bayes system.