The relation between semantics and syntax and where they
are represented in the neural level has been extensively debated
in neurosciences. Existing methods use manually designed
stimuli to distinguish semantic and syntactic information
in a sentence that may not generalize beyond the experimental
setting. This paper proposes an alternative framework
to study the brain representation of semantics and syntax.
Specifically, we embed the highly-controlled stimuli as objective
functions in learning sentence representations and propose
a disentangled feature representation model (DFRM) to
extract semantic and syntactic information in sentences. This
model can generate one semantic and one syntactic vector for
each sentence. Then we associate these disentangled feature
vectors with brain imaging data to explore brain representation
of semantics and syntax. Results have shown that semantic
feature is represented more robustly than syntactic feature
across the brain including the default-mode, frontoparietal,
visual networks, etc.. The brain representations of semantics
and syntax are largely overlapped, but there are brain regions
only sensitive to one of them. For instance, several frontal and
temporal regions are specific to the semantic feature; parts of
the right superior frontal and right inferior parietal gyrus are
specific to the syntactic feature.