On Representing Governed Prepositions and Handling "Incorrect" and Novel Prepositions

Hatte Blejer, Sharon Flank, Andrew Kehler

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.