An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Using corpus statistics and WordNet relations for sense identification
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
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
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This paper presents an ”one fit all” solution for any field's text Word Sense Disambiguation(WSD), with a Sense Rank AALest algorithm derived from the Adapted of Lesk's dictionary-based WSD algorithm. AALesk brings a score for different relationship during gloss comparing, which makes WSD not only based on statistical calculate by process in a semantic way. Rather than simply disambiguate one word's sense one time, our solution considers the whole sentence environment and uses a Sense Rank algorithm to speed up the whole procedure. Sense Rank weights different sense combination according to their importance score. All these contribute to the accuracy and effective of the solution. We evaluated our solution by using the English lexical sample data from the SENSEVAL-2 word sense disambiguation exercise and attains a good result. Additionally, the independence of system components also make our solution adaptive for different field's requirement and can be easily improved it's accuracy by changing its core algorithm AALesk's parameter setting.