Acquisition of Semantic Lexicons

Using Word Sense Disambiguation to Improve Precision
  • Bonnie J. Dorr
  • Doug Jones
Part of the Text, Speech and Language Technology book series (TLTB, volume 10)


This paper addresses the problem of large-scale acquisition of computational-semantic lexicons from machine-readable resources. We describe semantic filters designed to reduce the number of incorrect assignments (i.e., improve precision) made by a purely syntactic technique. We demonstrate that it is possible to use these filters to build broad-coverage lexicons with minimal effort, at a depth of knowledge that lies at the syntax-semantics interface. We report on our results of disambiguating the verbs in the semantic filters by adding WordNet sense annotations. We then show the results of our classification on unknown words and we evaluate these results.


Natural Language Processing Machine Translation Word Sense Correct Assignment Word Sense Disambiguation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Bonnie J. Dorr
    • 1
  • Doug Jones
    • 2
  1. 1.Department of Computer Science and Institute for Advanced Computer StudiesUniversity of MarylandUSA
  2. 2.U.S. Department of DefenseNatural Language Processing Research BranchUSA

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