Paper: Word Independent Context Pair Classification Model For Word Sense Disambiguation

ACL ID W05-0605
Title Word Independent Context Pair Classification Model For Word Sense Disambiguation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

Traditionally, word sense disambiguation (WSD) involves a different context classifi- cation 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 classi- fying 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 en- tropy modeling is used to train the word in- dependen...