DIMACS TR: 2002-10
Use of virtual semantic headers to improve the coverage of natural language question answering domains
Authors: Boris Galitsky
ABSTRACT
We report on the knowledge representation mechanism designed for natural
language question-answering system to function in such poorly-formalized
and heterogeneous domains as the financial, legal, pharmaceutical and
psychological. The system is oriented to provide a customized expert
advice, given the pre-designed set of textual templates and the database
that contains customers’ profiles and preferences, parameters of
products and services, etc. Question-answering is based on matching a
formal representation of a query against the formalized representations
of answers’ essential ideas (semantic headers of these answers).
Semantic headers are designed to be independent on the query phrasing and
are the means of approximate reasoning while generating the most relevant
advice. A semantic skeleton of an answer includes semantic headers and
deductive links between them and their entities, based on the
common-sense domain knowledge. Semantic skeleton includes the virtual
semantic headers, which do not have to be explicitly programmed but are
generated on the fly, using the clauses of semantic skeleton, to be
matched with a question.
We present the evaluation of the released question-answering system,
advising the customers of H&R Block and CBS Market Watch on various taxes
and associated legal issues starting from 1999. Domain development and
maintenance implications of semantic header technique, answer accuracy,
meaning deviations and overall customer impressions are analyzed.
Paper Available at:
ftp://dimacs.rutgers.edu/pub/dimacs/TechnicalReports/TechReports/2002/2002-10.ps.gz
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