  Word vectors are at the core of many natural language processing tasks.
Recently, there has been interest in post-processing word vectors to enrich
their semantic information. In this paper, we introduce a novel word vector
post-processing technique based on matrix conceptors (Jaeger2014), a family of
regularized identity maps. More concretely, we propose to use conceptors to
suppress those latent features of word vectors having high variances. The
proposed method is purely unsupervised: it does not rely on any corpus or
external linguistic database. We evaluate the post-processed word vectors on a
battery of intrinsic lexical evaluation tasks, showing that the proposed method
consistently outperforms existing state-of-the-art alternatives. We also show
that post-processed word vectors can be used for the downstream natural
language processing task of dialogue state tracking, yielding improved results
in different dialogue domains.
