Publications
OTHERS (INTERNATIONAL) Incremental Skip-gram Model with Negative Sampling
Nobuhiro Kaji, Hayato Kobayashi
arXiv.org
April 14, 2017
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.
Paper :
Incremental Skip-gram Model with Negative Sampling
(external link)