[Classic] Word2Vec: Distributed Representations of Words and Phrases and their Compositionality

#ai #research #word2vec

Word vectors have been one of the most influential techniques in modern NLP to date. This paper describes Word2Vec, which the most popular technique to obtain word vectors. The paper introduces the negative sampling technique as an approximation to noise contrastive estimation and shows that this allows the training of word vectors from giant corpora on a single machine in a very short time.

OUTLINE:
0:00 - Intro & Outline
1:50 - Distributed Word Representations
5:40 - Skip-Gram Model
12:00 - Hierarchical Softmax
14:55 - Negative Sampling
22:30 - Mysterious 3/4 Power
25:50 - Frequent Words Subsampling
28:15 - Empirical Results
29:45 - Conclusion & Comments

Paper: https://arxiv.org/abs/1310.4546
Code: https://code.google.com/archive/p/word2vec/

Abstract:
The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of "Canada" and "Air" cannot be easily combined to obtain "Air Canada". Motivated by this example, we present a simple method for finding phrases in text, and show that learning good vector representations for millions of phrases is possible.

Authors: Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, Jeffrey Dean

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