A Graph-based Approach to Word Sense Disambiguation. An Unsupervised Method Based on Semantic Relatedness

Word Sense Disambiguation (WSD) is the task of automatically choosing the correct meaning of a word in a context. Due to the importance of this task, it is considered as one of the most important and challenging problems in the field of computational linguistics and plays a crucial role in various natural language processing (NLP) applications. In this paper, we present an improved version of a recent unsupervised graphbased word sense disambiguation method considered to be one of the states of the art techniques. Using WordNet as our knowledge-base, we introduce a new method of combining similarity metrics that uses higher order relations between words to assign appropriate weights to each edge in the graph. Furthermore, we propose a new approach for selecting the most appropriate sense of the target word that makes use of the indegree centrality algorithm and senses of the neighbor words. Experimental results on benchmark datasets Senseval-2 and Senseval-3 shows that the proposed model outperforms all other graph-based methods presented in the literature.