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A corpus-based analysis of argument realization by preposition structures

Published online by Cambridge University Press:  01 July 2009

QIBO ZHU
QIBO ZHU*
Affiliation:
Statistics Canada, RHC-10Q, 150 Tunney's Pasture Driveway, Ottawa, Ontario K1A 0T6, Canada and Institute of Cognitive Science, Carleton University, Room 2201, Dunton Tower, 1125 Colonel By Drive, Ottawa, Ontario K1S 5B6, Canada e-mail: [email protected]; [email protected]
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Abstract

This article studies the issue of argument realization by preposition structures. By examining the preposition structures that are marked as frame elements in FrameNet, the article attempts to give corpus-based attestations to the hypothesized link between deep semantic arguments and their surface syntactic representations. Problems addressed in this article include how argument realization by preposition structures can be predictable from the target lexical unit and the frame it evokes, and why some noncentral prepositions get selected in the argument realization options. The investigation is primarily inspired by Fillmore's work in frame semantics. The source data for this study is derived from a preposition knowledge base that we have recently built by extracting all the semantically annotated preposition structures in FrameNet. The analysis shows that while there are various semantic–syntactic mapping possibilities, for most semantic arguments, the tendency of using central prepositions in their realization expressions is very strong. This is a clear indication that some preposition structures are linked to certain semantic arguments more than they are to others. A similar experiment was conducted using the annotated PropBank corpus to corroborate the supporting evidence found in FrameNet. The results of this study, together with the syntactic–semantic mapping lists of preposition structures can provide raw linguistic data for the study of preposition semantics, lexicography, argument realization, word sense disambiguation, and natural language understanding.

Type
Papers
Information
Natural Language Engineering , Volume 15 , Issue 3 , July 2009 , pp. 379 - 414
Copyright
Copyright © Cambridge University Press 2009

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