  Lexical ambiguity can impede NLP systems from accurate understanding of
semantics. Despite its potential benefits, the integration of sense-level
information into NLP systems has remained understudied. By incorporating a
novel disambiguation algorithm into a state-of-the-art classification model, we
create a pipeline to integrate sense-level information into downstream NLP
applications. We show that a simple disambiguation of the input text can lead
to consistent performance improvement on multiple topic categorization and
polarity detection datasets, particularly when the fine granularity of the
underlying sense inventory is reduced and the document is sufficiently large.
Our results also point to the need for sense representation research to focus
more on in vivo evaluations which target the performance in downstream NLP
applications rather than artificial benchmarks.
