A long-standing goal for the field of artificial intelligence is to
enable computer understanding of human languages. A core requirement
in reaching this goal is the ability to transform individual sentences
into a form better suited for computer manipulation. This ability,
called semantic parsing, requires several knowledge sources, such as a
grammar, lexicon, and parsing mechanism.
Building natural language parsing systems by hand is a tedious,
error-prone undertaking. We build on previous research in automating
the construction of such systems using machine learning techniques.
The result is a combined system that learns semantic lexicons and
semantic parsers from one common set of training examples. The input
required is a corpus of sentence/representation pairs, where the
representations are in the output format desired. A new system,
WOLFIE, learns semantic lexicons to be used as background knowledge by
a previously developed parser acquisition system, CHILL. The combined
system is tested on a real world domain of answering database queries.
We also compare this combination to a combination of CHILL with a
previously developed lexicon learner, demonstrating superior
performance with our system. Finally, we show the ability of the
system to learn to process natural languages other than English.
One difficulty in using machine learning methods for building natural
language interfaces is building the required annotated corpus. Therefore, we
also address this issue by using active learning to reduce the
number of training examples required by both WOLFIE and CHILL.
Experimental results show that the number of examples needed to reach
a given level of performance can be significantly reduced with this method.
Date: Thurs., October 15; Time: 4:15-5:30PM; Place: Gates 104
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