Encoding Prior Knowledge with Eigenword Embeddings

Dominique Osborne; Shashi Narayan; Shay B. Cohen

Encoding Prior Knowledge with Eigenword Embeddings

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arxiv 1509.01007 v3 pith:N7DGW2ZZ submitted 2015-09-03 cs.CL

Dominique Osborne , Shashi Narayan , Shay B. Cohen This is my paper · ORCID

classification cs.CL

keywords embeddingswordindicatesknowledgepriorviewanalysisbeen

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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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