摘要
Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using Wikipedia as the external knowledge source to decipher the true meaning of each term using a single term as the context. We experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. We also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%.
Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using Wikipedia as the external knowledge source to decipher the true meaning of each term using a single term as the context. We experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. We also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%.
