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arXiv · 1509.01007

Encoding Prior Knowledge with Eigenword Embeddings

Dominique Osborne·Shashi Narayan·Shay B. Cohen
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Abstract

Canonical correlation analysis (CCA) is a method for reducing the dimension of data represented using two views. It has been previously used to derive word embeddings, where one view indicates a word, and the other view indicates its context. We describe a way to incorporate prior knowledge into CCA, give a theoretical justification for it, and test it by deriving word embeddings and evaluating them on a myriad of datasets.

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Dominique Osborne, Shashi Narayan, Shay B. Cohen. 2016-07-27. Encoding Prior Knowledge with Eigenword Embeddings. https://arxiv.org/abs/1509.01007

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