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 graph based 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 in degree 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.
2016 24th Iranian Conference on Electrical Engineering (ICEE)