On representing governed prepositions and handling "incorrect" and novel prepositions

Authors:
Hatte R. Blejer;Sharon Flank;Andrew Kehler
Affiliations:
SRA Corporation, North Arlington, VA;SRA Corporation, North Arlington, VA;SRA Corporation, North Arlington, VA
Venue:
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Year:
1989

Citing 6
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Abstract

NLP systems, in order to be robust, must handle novel and ill-formed input. One common type of error involves the use of non-standard prepositions to mark arguments. In this paper, we argue that such errors can be handled in a systematic fashion, and that a system designed to handle them offers other advantages. We offer a classification scheme for preposition usage errors. Further, we show how the knowledge representation employed in the SRA NLP system facilitates handling these data.