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 lowrank 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.
CitationMadhyastha, P.S.; Carreras, X.; Quattoni, A. Learning task-specific bilexical embeddings. A: International Conference on Computational Linguistics. "Proceedings of the 25th International Conference on Computational Linguistics". Dublin: 2014, p. 161-171.
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