Impact of feature selection for corpus-based WSD in Turkish
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.