Paper: Learning Task-specific Bilexical Embeddings

ACL ID C14-1017
Title Learning Task-specific Bilexical Embeddings
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

We present a method that learns bilexical operators over distributional representations of words and leverages supervised data for a linguistic relation. The learning algorithm exploits low- rank bilinear forms and induces low-dimensional embeddings of the lexical space tailored for the target linguistic relation. An advantage of imposing low-rank constraints is that prediction is expressed as the inner-product between low-dimensional embeddings, which can have great computational benefits. In experiments with multiple linguistic bilexical relations we show that our method effectively learns using embeddings of a few dimensions.