Using Knowledge-Based Constraints to Improve Question-Answering Accuracy
John M. Prager
IBM T.J. Watson Research Ctr.

Most Question-Answering systems use a combination of statistical and
symbolic techniques: for example, almost all use a search component,
which fetches documents and/or passages using statistical matching
formulae, and answer selection techniques which are often more
linguistically-informed.  The QA system at IBM Research, which has
performed well in TREC-QA over the years, is no different in those
respects, but we have at the same time been exploring various
knowledge-based filtering techniques to constrain candidate answers.
I will describe three such techniques.  The first, which has been part
of our core system since 1999, is what we call Predictive Annotation,
a form of semantic indexing in which the answer type is a required
term in the search engine query, greatly reducing the number of
passages that need to be considered.  QA-by-Dossier asks additional
questions to the one from the user, and enforces real-world
constraints between the different questions and answers, on the
assumption that only correct answers will provide a consistent model.
Finally, Question Inversion is a specific form of QA-by-Dossier in
which initial candidate answers are inserted into a reformulated
question with a term removed, with the expectation that only the
correct answer will allow the removed term to be recovered.  I will
present experimental results from using these techniques, and discuss
their pros and cons.