Word Sense Discovery for Web Information Retrieval

Word meaning disambiguation has always been an important problem in many computer science tasks, such as information retrieval and extraction. One of the problems,faced in automatic word sense discovery, is the number of different senses a word can have. Often, senses are dominated by some other, more frequent ones. Discovering such dominated meanings can significantly improve quality of many text-related algorithms. In particular, web search quality can be leveraged. In the paper, we present a novel approach for discovering word senses. The method is based on concise representations of frequent patterns. The method attempts to discover not only word senses that are dominating, but also senses that are dominated and underrepresented in the repository.