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Combining independent modules to solve multiple-choice synonym and analogy problems
Combining independent modules to solve multiple-choice synonym and analogy problems
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Combining independent modules to solve multiple-choice synonym and analogy problems
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Combining independent modules to solve multiple-choice synonym and analogy problems
(Indexer Terms)
Existing 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.
2003
Combining independent modules to solve multiple-choice synonym and analogy problems
Language
Machine Learning
Statistical Models
Computational Linguistics
Semantics
Bigham
Jeffrey
Jeffrey Bigham
Shnayder
Victor
Victor Shnayder
Littman
Michael
Michael Littman
Turney
Peter
Peter Turney