Paper: Glove: Global Vectors for Word Representation

ACL ID D14-1162
Title Glove: Global Vectors for Word Representation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arith- metic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global log- bilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word co- occurrence matrix, rather than on the en- tire sparse matrix or on individual context windows in a large corpus. The model pro- duces a vector space w...