The descent of hierarchy, and selection in relational semantics

  • Authors:
  • Barbara Rosario;Marti A. Hearst;Charles Fillmore

  • Affiliations:
  • SIMS, UC Berkeley, Berkeley, CA;SIMS, UC Berkeley, Berkeley, CA;ICSI, UC Berkeley, Berkeley, CA

  • Venue:
  • ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2002

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Abstract

In many types of technical texts, meaning is embedded in noun compounds. A language understanding program needs to be able to interpret these in order to ascertain sentence meaning. We explore the possibility of using an existing lexical hierarchy for the purpose of placing words from a noun compound into categories, and then using this category membership to determine the relation that holds between the nouns. In this paper we present the results of an analysis of this method on two-word noun compounds from the biomedical domain, obtaining classification accuracy of approximately 90%. Since lexical hierarchies are not necessarily ideally suited for this task, we also pose the question: how far down the hierarchy must the algorithm descend before all the terms within the subhierarchy behave uniformly with respect to the semantic relation in question? We find that the topmost levels of the hierarchy yield an accurate classification, thus providing an economic way of assigning relations to noun compounds.