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

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dc.creatorTurney, Peter D.
dc.creatorLittman, Michael L.
dc.creatorBigham, Jeffrey
dc.creatorShnayder, Victor
dc.date2003-09-19
dc.date.accessioned2026-07-07T03:20:21Z
dc.date.available2026-07-07T03:20:21Z
dc.descriptionExisting 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.
dc.description8 pages, related work available at http://www.cs.rutgers.edu/~mlittman/ and http://purl.org/peter.turney/
dc.identifierhttps://arxiv.org/abs/cs/0309035
dc.identifierhttp://arxiv.org/abs/cs/0309035
dc.identifierProceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP-03), (2003), Borovets, Bulgaria, 482-489
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31797
dc.subjectComputation and Language
dc.subjectInformation Retrieval
dc.subjectMachine Learning
dc.subjectI.2.6; I.2.7; H.3.1; J.5
dc.titleCombining Independent Modules to Solve Multiple-choice Synonym and Analogy Problems
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