A Hybrid Approach to Word Sense Disambiguation
Deepti Goyal, Deepika Goyal, Manjeet Singh · 2010
Words can have more than one distinct meaning called as polysemous words. This paper concentrates on Word Sense Disambiguation (WSD) which refers to the resolution of lexical ambiguity that arises when a given word has several different meanings. The paper presents a hybrid approach for this problem based on the basic principle by Yarowsky’s unsupervised algorithm for WSD. It also employs Naïve Baye’s theorem to find the likelihood ratio of the sense in the given context. This way, the approach preserves the advantage of principles of Yarowsky’s (one sense per discourse and one sense per collocation) and utilizes Baye’s theorem for the better performance of the system. The seed/sense selection can be done either manually or automatically to find the local or global dependency of a given sense in a given window. To find the local dependency, the system uses the definitions provided by the dictionary (Word Net) for the target word whereas the global dependency is determined on the basis of fact that the word that occurs with significantly higher frequency in an entire corpus can be used as seed word. The proposed approach is applied on some ambiguous words for which training and test data is developed and the performance of the system is determined, listed in the form of tables. Finally the comparison is made between the two seed selection methods i.e. local and global. I.