CORPUS-BASED APPROACHES TO SEMANTIC INTERPRETATION IN NATURAL-LANGUAGE PROCESSING

In recent years, there has been a flurry of research into empirical, c orpus-based learning approaches to natural language processing (NLP). Most empirical NLP work to date has focused on relatively low-level la nguage processing such as part-of-speech lagging, text segmentation, a nd syntactic parsing. The success of these approaches has stimulated r esearch in using empirical learning techniques in other facets of NLP, including semantic analysis-uncovering the meaning of an utterance. T his article is an introduction to some of the emerging research in the application of corpus-based learning techniques to problems in semant ic interpretation. In particular, we focus on two important problems i n semantic interpretation, namely, word-sense disambiguation and seman tic parsing.