Pragmatics and natural language generation
Artificial Intelligence
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Semantic representations of near-synonyms for automatic lexical choice
Semantic representations of near-synonyms for automatic lexical choice
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
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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.