The distributional hypothesis of Harris (1954) , according to which the meaning of words is evidenced by the contexts they occur in, has motivated several effective techniques for obtaining vector space semantic representations of words using unannotated text corpora. This paper argues that lexico-semantic content should additionally be invariant across languages and proposes a simple technique based on canonical correlation analysis (CCA) for incorporating multilingual evidence into vectors generated monolingually. We evaluate the resulting word representations on standard lexical semantic evaluation tasks and show that our method produces substantially better semantic representations than monolingual techniques.
Faruqui & Dyer REF use the distributional invariance across languages and propose a technique based on canonical correlation analysis (CCA) for merging multilingual evidence into vectors generated monolingually.
Improving Vector Space Word Representations Using Multilingual Correlation
EACL
Computer Science