Evaluating distributional models of semantics for syntactically invariant inference

• Authors: Jackie Ck Cheung;Gerald Penn
• Venue: EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
• Year: 2012

A major focus of current work in distributional models of semantics is to construct phrase representations compositionally from word representations. However, the syntactic contexts which are modelled are usually severely limited, a fact which is reflected in the lexical-level WSD-like evaluation methods used. In this paper, we broaden the scope of these models to build sentence-level representations, and argue that phrase representations are best evaluated in terms of the inference decisions that they support, invariant to the particular syntactic constructions used to guide composition. We propose two evaluation methods in relation classification and QA which reflect these goals, and apply several recent compositional distributional models to the tasks. We find that the models outperform a simple lemma overlap baseline slightly, demonstrating that distributional approaches can already be useful for tasks requiring deeper inference.