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"Combining independent modules to solve multiple-choice synonym and analogy problems\n"^^ .
"Existing statistical approaches to natural language problems are very \ncoarse approximations to the true complexity of language processing.\nAs such, no single technique will be best for all problem instances. \nMany researchers are examining ensemble methods that combine the\noutput of successful, separately developed modules to create more \naccurate solutions. This paper examines three merging rules for \ncombining probability distributions: the well known mixture rule, the \nlogarithmic rule, and a novel product rule. These rules were applied \nwith state-of-the-art results to two problems commonly used to assess \nhuman mastery of lexical semantics -- synonym questions and analogy\nquestions. All three merging rules result in ensembles that are more \naccurate than any of their component modules. The differences among the\nthree rules are not statistically significant, but it is suggestive \nthat the popular mixture rule is not the best rule for either of the \ntwo problems."^^ .
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"Jeffrey"^^ .
"Bigham"^^ .
"Jeffrey Bigham"^^ .
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"Michael"^^ .
"Littman"^^ .
"Michael Littman"^^ .
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"Peter"^^ .
"Turney"^^ .
"Peter Turney"^^ .
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"Victor"^^ .
"Shnayder"^^ .
"Victor Shnayder"^^ .
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"Combining independent modules to solve multiple-choice synonym and analogy problems\n (PDF)"^^ .
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"ranlp-03-final-version.pdf"^^ .
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"Combining independent modules to solve multiple-choice synonym and analogy problems\n (Image (PNG))"^^ .
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"Combining independent modules to solve multiple-choice synonym and analogy problems\n (Indexer Terms)"^^ .
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"HTML Summary of #3163 \n\nCombining independent modules to solve multiple-choice synonym and analogy problems\n\n\n" .
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"Statistical Models" .
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"Language" .
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"Computational Linguistics" .
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"Semantics" .
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"Machine Learning" .
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