Syntax, preference, and right attachment

Authors:
Yorick Wilks;Xiuming Huang;Dan Fass
Affiliations:
Computing Research Laboratory, New Mexico State University, Las Cruces, NM;Computing Research Laboratory, New Mexico State University, Las Cruces, NM;Computing Research Laboratory, New Mexico State University, Las Cruces, NM
Venue:
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 2
Year:
1985

Citing 3
Cited 3

Understanding Natural Language

Understanding Natural Language
Preference semantics, ill-formedness, and metaphor

Computational Linguistics - Special issue on ill-formed input
On parsing preferences

ACL '84 Proceedings of the 10th International Conference on Computational Linguistics and 22nd annual meeting on Association for Computational Linguistics

Parsing long English sentences with pattern rules

COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 3
A cognitively plausible approach to understanding complex syntax

AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Hybrid preference machines based on inspiration from neuroscience

Cognitive Systems Research

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Abstract

The paper claims that the right attachment rules for phrases originally suggested by Frazier and Fodor are wrong, and that none of the subsequent patchings of the rules by syntactic methods have improved the situation. For each rule there are perfectly straightforward and indefinitely large classes of simple counterexamples. We then examine suggestions by Ford et al., Schubert and Hirst which are quasi-semantic in nature and which we consider ingenious but unsatisfactory. We offer a straightforward solution within the framework of preference semantics, and argue that the principal issue is not the type and nature of information required to get appropriate phrase attachments, but the issue of where to store the information and with what processes to apply it. We present a prolog implementation of a best first algorithm covering the data and contrast it with closely related ones, all of which are based on the preferences of nouns and prepositions, as well as verbs.