Corpus-Based Approaches to Semantic Interpretation in NLP

  • Hwee Tou Ng
  • John Zelle


In recent years, there has been a flurry of research into empirical, corpus-based learning approaches to natural language processing (NLP). Most empirical NLP work to date has focused on relatively low-level language processing such as part-of-speech tagging, text segmentation, and syntactic parsing. The success of these approaches has stimulated research in using empirical learning techniques in other facets of NLP, including semantic analysis -- uncovering the meaning of an utterance. This article is an introduction to some of the emerging research in the application of corpus-based learning techniques to problems in semantic interpretation. In particular, we focus on two important problems in semantic interpretation, namely, word-sense disambiguation and semantic parsing.
How to Cite
Ng, H. T., & Zelle, J. (1997). Corpus-Based Approaches to Semantic Interpretation in NLP. AI Magazine, 18(4), 45.