Corpus-based sense disambiguation methods, like most other statistical NLP approaches, suffer from the problem of data sparseness. In this paper, we describe an approach which overcomes this problem using dictionary definitions. Using the definitionbased conceptual co-occurrence data collected from the relatively small Brown corpus, our sense disambiguation system achieves an average accuracy comparable to human performance given the same contextual information.
In REF , using the dictionary definition, co-occurrence data of concepts, rather than words, is collected from a relatively small corpus to tackle the data sparseness problem.
Statistical Sense Disambiguation With Relatively Small Corpora Using Dictionary Definitions
Annual Meeting Of The Association For Computational Linguistics
Computer Science