Tagging for learning: collecting thematic relations from corpus

  • Authors:
  • Uri Zernik;Paul Jacobs

  • Affiliations:
  • Artificial Intelligence Program, GE Research and Development Center, Schenectady, NY;Artificial Intelligence Program, GE Research and Development Center, Schenectady, NY

  • Venue:
  • COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 1
  • Year:
  • 1990

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Abstract

Recent work in text analysis has suggested that data on words that frequently occur together reveal important information about text content. Co-occurrence relations can serve two main purposes in language processing. First, the statistics of co-occurrence have been shown to produce accurate results in syntactic analysis. Second, the way that words appear together can help in assigning thematic roles in semantic interpretation. This paper discusses a method for collecting co-occurrence data, acquiring lexical relations from the data, and applying these relations to semantic analysis.