Building a Lexical KnowledgeBase of NearSynonym Differences: Automatic Knowledge Acquisition,Used

Building a Lexical KnowledgeBase of NearSynonym Differences: Automatic Knowledge Acquisition,Used

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Current natural language generation or machine translation systems cannot distinguish among nearsynonyms words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between nearsynonyms in existing computational lexical resources. In this work, I automatically acquired a lexical knowledgebase of nearsynonym differences from multiple sources, using an unsupervised decision list algorithm. The main types of differences are: stylistic (for example, "inebriated" is more formal than "drunk"), attitudinal (for example, "skinny" is more pejorative than "slim"), and denotational (for example, "blunder" implies "accident" and "ignorance", while "error" does not). To show how the knowledgebase can be used in practice, I designed Xenon, a natural language generation system system that chooses the nearsynonym that best matches a set of input preferences. I implemented Xenon by adding a nearsynonym choice module and a nearsynonym collocation module to an existing generalpurpose surface realizer.

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