Word independent context pair classification model for Word Sense Disambiguation

Traditionally, word sense disambiguation (WSD) involves a different context classification model for each individual word. This paper presents a weakly supervised learning approach to WSD based on learning a word independent context pair classification model. Statistical models are not trained for classifying the word contexts, but for classifying a pair of contexts, i.e. determining if a pair of contexts of the same ambiguous word refers to the same or different senses. Using this approach, annotated corpus of a target word A can be explored to disambiguate senses of a different word B. Hence, only a limited amount of existing annotated corpus is required in order to disambiguate the entire vocabulary. In this research, maximum entropy modeling is used to train the word independent context pair classification model. Then based on the context pair classification results, clustering is performed on word mentions extracted from a large raw corpus. The resulting context clusters are mapped onto the external thesaurus WordNet. This approach shows great flexibility to efficiently integrate heterogeneous knowledge sources, e.g. trigger words and parsing structures. Based on Senseval-3 Lexical Sample standards, this approach achieves state-of-the-art performance in the unsupervised learning category, and performs comparably with the supervised Naïve Bayes system.