Inkpen and Hirst Abstract

• Inkpen, Diana Zaiu and Hirst, Graeme.
  ``Experiments on extracting knowledge from a machine-readable dictionary of synonym differences.''
  In: Gelbukh, Alexander (editor), Computational Linguistics and Intelligent Text Processing (Proceedings, Second Conference on Intelligent Text Processing and Computational Linguistics, Mexico City, February 2001), Lecture Notes in Computer Science 2004, Berlin: Springer-Verlag, 2001, 264--278.

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 lexical knowledge-base from the Choose the Right Word dictionary of near-synonym discrimination. We do this by automatically classifying sentences in this dictionary 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 selected clusters against a manually built standard solution, and compare them with the results of a baseline algorithm.

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