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

TL;DR

This paper explores ensemble methods for combining modules to improve accuracy in solving synonym and analogy problems, demonstrating that ensemble approaches outperform individual modules.

Contribution

It introduces and compares three merging rules, including a novel product rule, for combining probability distributions in lexical semantic tasks.

Findings

All three merging rules improve accuracy over individual modules.

No statistically significant difference among the merging rules.

The mixture rule may not be the optimal choice for these problems.

Abstract

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.