A knowledge-based word sense disambiguation algorithm utilizing syntactic dependency relation

Accurate recognition of the meaning of words in the given document fragments has a long historical standing in computational linguistics. Because of its importance in understanding natural language semantics, it becomes one of the most challenging issues within this field. In this paper, we present an enhanced knowledge-based word sense disambiguation (WSD) algorithm that manipulates by calculating the semantic similarity between definitions of the target word and an extended syntactic context vector. The actual meaning of the target word is identified as that for which the semantic similarity between definition and extended syntactic context vector is highest. Therefore, while traditional WSD approaches are based on semantic information, our method is built on both semantic and syntactic relations between sentences. This allows it to take advantage of a better use of comprehensive knowledge and more in line with the human being way of thinking. For the sake of improving the performance of WSD, we combine the syntactic importance of the words within the original document piece to provide a sequence that can be used to decide which words should be disambiguated. Finally, the evaluation performed on two standard datasets shows that our algorithm goes better beyond the baselines and has more desirable features than other popular algorithms.