Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
Jeffrey Pennington,Richard Socher,Christopher D. Manning +2 more
- pp 1532-1543
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TLDR
A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.Abstract:
Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, 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 logbilinear 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 cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.read more
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References
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Proceedings ArticleDOI
Word Embeddings through Hellinger PCA
Rémi Lebret,Ronan Collobert +1 more
TL;DR: The authors proposed to simplify the word embeddings computation through a Hellinger PCA of the word co-occurence matrix, which can provide an easy way to adapt embedding to specific tasks.
Proceedings Article
Effect of Non-linear Deep Architecture in Sequence Labeling
TL;DR: The close connection between CRF and “sequence model” neural nets is shown, and an empirical investigation to compare their performance on two sequence labeling tasks is presented, suggesting that non-linear models are highly effective in low-dimensional distributional spaces.