Vector-space models for PPDB paraphrase ranking in context

Abstract

The PPDB is an automatically built database which contains millions of paraphrases in different languages. Paraphrases in this resource are associated with features that serve to their ranking and reflect paraphrase quality. This context-unaware ranking captures the semantic similarity of paraphrases but cannot serve to estimate their adequacy in specific contexts. We propose to use vector-space semantic models for selecting PPDB paraphrases that preserve the meaning of specific text fragments. This is the first work that addresses the substitutability of PPDB paraphrases in context. We show that vector-space models of meaning can be successfully applied to this task and increase the benefit brought by the use of the PPDB resource in applications.