Word sense disambiguation based on dependency constraint knowledge

Abstract

The performance of knowledge-based word sense disambiguation (WSD) is confused with the acquisition of knowledge base and the selection of related feature words. It is difficult to automatically construct high-quality knowledge base and precisely select related words of ambiguous word. Aiming at the problems, the paper proposes word sense disambiguation based on dependency constraint knowledge. The method fully utilizes the advantage of dependency parsing to solve the confusions of knowledge-based WSD. A large-scale corpus is parsed to construct a high-quality dependency constraint knowledge base. The sentence is parsed to obtain the precise dependency constraint cells for each ambiguous word. Based on dependency constraint knowledge base, posterior probability of each sense of ambiguous word on dependency constraint cells is computed. The sense with max posterior probability is selected as right sense. The method has achieved the best performance among unsupervised and knowledge-based methods in SemEval dataset.