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

This work uses Edmonds's model of lexical knowledge to represent clusters of near-synonyms and uses a decision-list learning algorithm to learn words and expressions that characterize the classes DENOTATIONAL DISTinCTIONS and ATTITUDE-STYLE DISTINCTIONS.

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.