AutoASC — A SYSTEM FOR AUTOMATIC ACQUISITION OF SENSE TAGGED CORPORA

Many natural language processing tasks, such as word sense disambiguation, knowledge acquisition, information retrieval, use semantically tagged corpora. Till recently, these corpus-based systems relied on text manually annotated with semantic tags; but the massive human intervention in this process has become a serious impediment in building robust systems. In this paper, we present AutoASC, a system which automatically acquires sense tagged corpora. It is based on (1) the information provided in WordNet, particularly the word definitions found within the glosses and (2) the information gathered from Internet using existing search engines. The system was tested on a set of 46 concepts, for which 2071 example sentences have been acquired; for these, a precision of 87% was observed.