Experiments on Extracting Knowledge from a Machine-Readable Dictionary of Synonym Differences
Identifieur interne : 001B38 ( Main/Exploration ); précédent : 001B37; suivant : 001B39Experiments on Extracting Knowledge from a Machine-Readable Dictionary of Synonym Differences
Auteurs : Diana Zaiu Inkpen [Canada, États-Unis] ; Graeme Hirst [Canada, États-Unis]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2001.
Abstract
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.
Url:
DOI: 10.1007/3-540-44686-9_28
Affiliations:
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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. 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