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Matching utterances to rich knowledge structures to acquire a model of the speaker's goal

Published:02 October 2005Publication History

ABSTRACT

An ultimate goal of AI is to build end-to-end systems that interpret natural language, reason over the resulting logical forms, and perform actions based on that reasoning. This requires systems from separate fields be brought together, but often this exposes representational gaps between them. The logical forms from a language interpreter may mirror the surface forms of utterances too closely to be usable as-is, given a reasoner's requirements for knowledge representations. What is needed is a system that can match logical forms to background knowledge flexibly to acquire a rich semantic model of the speaker's goal. In this paper, we present such a "matcher" that uses semantic transformations to overcome structural differences between the two representations. We evaluate this matcher in a MUC-like template-filling task and compare its performance to that of two similar systems.

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      • Published in

        cover image ACM Conferences
        K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture
        October 2005
        234 pages
        ISBN:1595931635
        DOI:10.1145/1088622

        Copyright © 2005 ACM

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 2 October 2005

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        Overall Acceptance Rate55of198submissions,28%

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