Neighbors Help: Bilingual Unsupervised WSD Using Context

Word Sense Disambiguation (WSD) is one of the toughest problems in NLP, and in WSD, verb disambiguation has proved to be extremely difficult, because of high degree of polysemy, too fine grained senses, absence of deep verb hierarchy and low inter annotator agreement in verb sense annotation. Unsupervised WSD has received widespread attention, but has performed poorly, specially on verbs. Recently an unsupervised bilingual EM based algorithm has been proposed, which makes use only of the raw counts of the translations in comparable corpora (Marathi and Hindi). But the performance of this approach is poor on verbs with accuracy level at 25-38%. We suggest a modification to this mentioned formulation, using context and semantic relatedness of neighboring words. An improvement of 17% 35% in the accuracy of verb WSD is obtained compared to the existing EM based approach. On a general note, the work can be looked upon as contributing to the framework of unsupervised WSD through context aware expectation maximization.