Combining Independent Modules to Solve Multiple-Choice Synonym and Analogy Problems

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TypeArticle
ConferenceProceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03), September 10-12, 2003., Borovets, Bulgaria
AbstractExisting statistical approaches to natural language problems are very coarse approximations to the true complexity of language processing. As such, no single technique will be best for all problem instances. Many researchers are examining ensemble methods that combine the output of successful, separately developed modules to create more accurate solutions. This paper examines three merging rules for combining probability distributions: the well known mixture rule, the logarithmic rule, and a novel product rule. These rules were applied with state-of-the-art results to two problems commonly used to assess human mastery of lexical semantics/synonym questions and analogy questions. All three merging rules result in ensembles that are more accurate than any of their component modules. The differences among the three rules are not statistically significant, but it is suggestive that the popular mixture rule is not the best rule for either of the two problems.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number46506
NPARC number8913366
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Record identifiere2d6c420-e830-4aa4-8e94-3d71a457252c
Record created2009-04-22
Record modified2016-05-09
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