An unsupervised method for word sense disambiguation

Abstract Word sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet lexical dictionary and the Wikipedia corpus. Initially, we find all the probable senses of the target word using WordNet. For each of the words present in a sense, we calculate the collocation extraction score with the other words in the sentence. The collocation extraction score finds the probability of the occurrence of two words together in the Wikipedia corpus. The maximum collocation extraction score assigns the proper sense for that context of the sentence. Our method is not limited to the bi-grams that are made up of only two consecutive words. Our method can find the probability of having two words together in a sentence when other words separate these two words. To compare our WSD method with current knowledge-based unsupervised and supervised systems, we use different Senseval and SemEval datasets for doing WSD on English words. Finally, the experimental analysis illustrates the significance of the proposed approach over many baseline and current systems.