title: A novel approach to word sense disambiguation based on topical and semantic association.
abstract: Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches.
authors.affiliation: College of Computer Science and Technology, Jilin University, Changchun 130012, China ; School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun 130012, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Wang
authors.forename: Wanli
authors.initials: W
authors.lastname: Zuo
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007802:Language; D009323:Natural Language Processing; D012660:Semantics