  The pervasive use of distributional semantic models or word embeddings in a
variety of research fields is due to their remarkable ability to represent the
meanings of words for both practical application and cognitive modeling.
However, little has been known about what kind of information is encoded in
text-based word vectors. This lack of understanding is particularly problematic
when word vectors are regarded as a model of semantic representation for
abstract concepts. This paper attempts to reveal the internal information of
distributional word vectors by the analysis using Binder et al.'s (2016)
brain-based vectors, explicitly structured conceptual representations based on
neurobiologically motivated attributes. In the analysis, the mapping from
text-based vectors to brain-based vectors is trained and prediction performance
is evaluated by comparing the estimated and original brain-based vectors. The
analysis demonstrates that social and cognitive information is better encoded
in text-based word vectors, but emotional information is not. This result is
discussed in terms of embodied theories for abstract concepts.
