Modeling Order in Neural Word Embeddings at Scale

Andrew Trask; David Gilmore; Matthew Russell

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arXiv · 1506.02338

Modeling Order in Neural Word Embeddings at Scale

Andrew Trask·David Gilmore·Matthew Russell

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Abstract

Natural Language Processing (NLP) systems commonly leverage bag-of-words co-occurrence techniques to capture semantic and syntactic word relationships. The resulting word-level distributed representations often ignore morphological information, though character-level embeddings have proven valuable to NLP tasks. We propose a new neural language model incorporating both word order and character order in its embedding. The model produces several vector spaces with meaningful substructure, as evidenced by its performance of 85.8% on a recent word-analogy task, exceeding best published syntactic word-analogy scores by a 58% error margin. Furthermore, the model includes several parallel training methods, most notably allowing a skip-gram network with 160 billion parameters to be trained overnight on 3 multi-core CPUs, 14x larger than the previous largest neural network.

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Andrew Trask, David Gilmore, Matthew Russell. 2015-06-08. Modeling Order in Neural Word Embeddings at Scale. https://arxiv.org/abs/1506.02338

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