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Long-distance dependencies without filler−gaps: a cognitive-functional alternative in Fluid Construction Grammar*

Published online by Cambridge University Press:  07 April 2014

REMI VAN TRIJP
REMI VAN TRIJP*
Affiliation:
Sony Computer Science Laboratory Paris
*
†Address for correspondence: Sony Computer Science Laboratory Paris, 6, Rue Amyot, 75005 Paris, France. e-mail: [email protected]
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Abstract

Long-distance dependencies are notoriously difficult to analyze in a formally explicit way because they involve constituents that seem to have been extracted from their canonical position in an utterance. The most widespread solution is to identify a gap at an extraction site and to communicate information about that gap to its filler, as in What_FILLERdid you see_GAP? This paper rejects the filler−gap solution and proposes a cognitive-functional alternative in which long-distance dependencies spontaneously emerge as a side effect of how grammatical constructions interact with each other for expressing different conceptualizations. The proposal is supported by a computational implementation in Fluid Construction Grammar that works for both parsing and production.

Keywords

Long-distance dependenciesdisplacementcognitive-functional language processinglanguage formalizationcomputational modeling
Type
Research Article
Information
Language and Cognition , Volume 6 , Issue 2 , June 2014 , pp. 242 - 270
Copyright
Copyright © UK Cognitive Linguistics Association 2014 

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Footnotes

*

While this paper was undergoing review, I learned the sad news of Ivan Sag’s passing away. His contributions to the field can hardly be overestimated, and it is with the utmost respect for his work that I disagree with his analysis of long-distance dependencies. The research reported in this paper has been conducted at and funded by the Sony Computer Science Laboratory Paris. I would like to thank Luc Steels, director of Sony CSL Paris, for his feedback and support. I also thank Pieter Wellens from the VUB AI-Lab for his recent additions to FCG that have made this implementation possible. I also thank Frank Richter (University of Tübingen) and Stefan Müller (Free University of Berlin) for helping me to better understand HPSG. Finally, I would like to thank the editors and reviewers of Language and Cognition for their efforts that have helped to improve this paper. All remaining errors are of course my own.

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