Incremental Skip-gram Model with Negative Sampling

Nobuhiro Kaji; Hayato Kobayashi

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arXiv · 1704.03956

Incremental Skip-gram Model with Negative Sampling

Nobuhiro Kaji·Hayato Kobayashi

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Abstract

This paper explores an incremental training strategy for the skip-gram model with negative sampling (SGNS) from both empirical and theoretical perspectives. Existing methods of neural word embeddings, including SGNS, are multi-pass algorithms and thus cannot perform incremental model update. To address this problem, we present a simple incremental extension of SGNS and provide a thorough theoretical analysis to demonstrate its validity. Empirical experiments demonstrated the correctness of the theoretical analysis as well as the practical usefulness of the incremental algorithm.

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Nobuhiro Kaji, Hayato Kobayashi. 2017-04-13. Incremental Skip-gram Model with Negative Sampling. https://arxiv.org/abs/1704.03956

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