Automatic generation of labelled data for word sense disambiguation

Abstract

In this thesis, we proposed and evaluated a method for performing word sense disambiguation. Unlike commonly used machine learning methods, the proposed method does not use manually labeled data for training classifiers in order to perform word sense disambiguation. In this method, we first extract the instances that the Synonyms or Hyprnyms appear from the AQUAINT collection using Managing Gigabytes. Compare their feature with feature of the instance to be predicted using K-nearest neighbors belong to is selected as the predicted sense. We evaluated the method on the nouns of the SENSEVAL-1 English Trainable Sample Task and SENSEVAL-2 English Lexical Sample Task and showed that the method performed well relative to the predictor that used the most common sense of the word as identified by WordNet as prediction.