Title: Incremental Skip-gram Model with Negative Sampling,
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.