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- W1660519191 abstract "Existing statistical approaches to natural language problems are verycoarse approximations to the true complexity of language processing.As such, no single technique will be best for all probleminstances. Many researchers are examining ensemble methods thatcombine the output of multiple modules tocreate more accurate solutions. This paper examines three mergingrules for combining probability distributions: the familiar mixturerule, the logarithmic rule, and a novel product rule.These rules were applied with state-of-the-art results to twoproblems used to assess human mastery of lexicalsemantics -- synonym questions and analogy questions. All threemerging rules result in ensembles that are more accurate than any oftheir component modules. The differences among the three rules are not statisticallysignificant, but it is suggestive that the popular mixture ruleis not the best rule for either of the two problems." @default.
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- W1660519191 date "2004-01-01" @default.
- W1660519191 modified "2023-10-17" @default.
- W1660519191 title "Combining independent modules in lexical multiple-choice problems" @default.
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- W1660519191 doi "https://doi.org/10.1075/cilt.260.11tur" @default.
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