  This paper presents the first study aimed at capturing stylistic similarity
between words in an unsupervised manner. We propose extending the continuous
bag of words (CBOW) model (Mikolov et al., 2013) to learn style-sensitive word
vectors using a wider context window under the assumption that the style of all
the words in an utterance is consistent. In addition, we introduce a novel task
to predict lexical stylistic similarity and to create a benchmark dataset for
this task. Our experiment with this dataset supports our assumption and
demonstrates that the proposed extensions contribute to the acquisition of
style-sensitive word embeddings.
