On Learning more Appropriate Selectional Restrictions

We present some variations affecting the association measure and thresholding on a technique for learning Selectional Restrictions from on-line corpora. It uses a wide-coverage noun taxonomy and a statistical measure to generalize the appropriate semantic classes. Evaluation measures for the Selectional Restrictions learning task are discussed. Finally, an experimental evaluation of these variations is reported.

• Publication year

  1995

• Venue

  Conference of the European Chapter of the Association for Computational Linguistics

• Publication date

  1995-02-09

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/976973.976990arXiv cmp-lg/9502009
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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