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. © Springer-Verlag Berlin Heidelberg 2001.
CITATION STYLE
Inkpen, D. Z., & Hirst, G. (2001). Experiments on extracting knowledge from a machine-readable dictionary of synonym differences. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2004, 265–280. https://doi.org/10.1007/3-540-44686-9_28
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