Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
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
Combining knowledge- and corpus-based word-sense-disambiguation methods
Journal of Artificial Intelligence Research
SemEval-2007 task 12: Turkish lexical sample task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
| Hi-index | 0.00 |
Word sense disambiguation (WSD) is an important intermediate stage for many natural language processing applications. The senses of an ambiguous word are the classification of usages for that word. WSD is basically a mapping function from a context to a set of applicable senses depending on various parameters. Resource selection, determination of senses for ambiguous words, decision of effective features, algorithms, and evaluation criteria are the major issues in a WSD system. This paper deals with the feature selection strategies for word sense disambiguation task in Turkish language. There are many different features that can contribute to the meaning of a word. These features can vary according to the metaphorical usages, POS of the word, pragmatics, etc. The observations indicated that detecting the critical features can contribute much than the learning methodologies.