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Experiments on Extracting Knowledge from a Machine-Readable Dictionary of Synonym Differences

  • Diana Zaiu Inkpen
  • Graeme Hirst
Conference paper
  • 534 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2004)

Abstract

In machine translation and natural language generation, making the wrong word choice from a set of near-synonyms can be imprecise or awkward, or convey unwanted implications. Using Edmonds’s model of lexical knowledge to represent clusters of near-synonyms, our goal is to automatically derive a lexi- cal knowledge-base from the Choose the Right Word dictionary of near-synonym discrimination. We do this by automatically classifying sentences in this dictio- nary according to the classes of distinctions they express. We use a decision-list learning algorithm to learn words and expressions that characterize the classes DENOTATIONAL DISTINCTIONS and ATTITUDE-STYLE DISTINCTIONS. These results are then used by an extraction module to actually extract knowledge from each sentence. We also integrate a module to resolve anaphors and word-to-word comparisons. We evaluate the results of our algorithm for several randomly se- lected clusters against a manually built standard solution, and compare them with the results of a baseline algorithm.

Keywords

Machine Translation Word Sense Disambiguation Class Hierarchy Baseline Algorithm Lexical Knowledge 
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-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Diana Zaiu Inkpen
    • 1
  • Graeme Hirst
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada

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