Abstract
Word sense disambiguation (WSD) is a significant field in computational linguistics as it is indispensable for many language understanding applications. Automatic processing of documents is made difficult because of the fact that many of the terms it contain ambiguous. Word Sense Disambiguation (WSD) systems try to solve these ambiguities and find the correct meaning. Genetic algorithms can be active to resolve this problem since they have been effectively applied for many optimization problems. In this paper, genetic algorithms proposed to solve the word sense disambiguation problem that can automatically select the intended meaning of a word in context without any additional resource. The proposed algorithm is evaluated on a collection of documents and produce's a lot of sense to the ambiguities word, the system creates dynamic, and up-todate word sense in a highly automatic method.
Highlights
Most of the people use the web to find some contents. At searching process they never worry about ambiguities that occur between words .An ambiguous word is a word that has a lot of meaning in different contexts [2]
Representation of the context in which an ambiguous word occurs has great effect to successfully applied machine learning methods for word sense disambiguation (WSD) problem [6].So to apply genetic algorithms to word sense disambiguation (WSD) the search space of the problem must be represented in suitable format
This paper proposed a word sense disambiguation method based on genetic algorithm
Summary
Most of the people use the web to find some contents. At searching process they never worry about ambiguities that occur between words .An ambiguous word is a word that has a lot of meaning in different contexts [2]. There are two approaches of WSD [4]: 1) Deep Approaches: This is built on world knowledge. Such knowledge is not existing in computers readable format except in some restricted domain so this approach is not very common. To avoid preparing annotated corpora, effort needs to be oriented to new approaches in the knowledge-based unsupervised direction, one of the current trends to address WSD as a combinatorial optimization problem.[8]. To avoid the problem of annotated corpora this paper presented an unsupervised approach based on genetic algorithms to solve the problem of ambiguity words that automatically find the sense of the word from the document's collection.
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