Sparse bilingual word representations for cross-lingual lexical entailment

Abstract

We introduce the task of cross-lingual lexical entailment, which aims to detect whether the meaning of a word in one language can be inferred from the meaning of a word in another language. We construct a gold standard for this task, and propose an unsupervised solution based on distributional word representations. As commonly done in the monolingual setting, we assume a word e entails a word f if the prominent context features of e are a subset of those of f. To address the challenge of comparing contexts across languages, we propose a novel method for inducing sparse bilingual word representations from monolingual and parallel texts. Our approach yields an F-score of 70%, and significantly outperforms strong baselines based on translation and on existing word representations.