A Competitve Attachment Model for Resolving Syntactic Ambiguities in
Natural Language Parsing
A Competitve Attachment Model for Resolving Syntactic Ambiguities in
Natural Language Parsing
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Date
1998-10-15
Authors
Stevenson, Suzanne Ava
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Abstract
Linguistic ambiguity is the greatest obstacle to achieving practical
computational systems for natural language understanding. By
contrast, people experience surprisingly little difficulty in
interpreting ambiguous linguistic input. This dissertation explores
distributed computational techniques for mimicking the human ability
to resolve syntactic ambiguities efficiently and effectively. The
competitive attachment theory of parsing formulates the processing of
an ambiguity as a competition for activation within a hybrid
connectionist network. Determining the grammaticality of an input
relies on a new approach to distributed communication that integrates
numeric and symbolic constraints on passing features through the
parsing network. The method establishes syntactic relations both
incrementally and efficiently, and underlies the ability of the model
to establish long-distance syntactic relations using only local
communication within a network. The competitive distribution of
numeric evidence focuses the activation of the network onto a
particular structural interpretation of the input, resolving
ambiguities. In contrast to previous approaches to ambiguity
resolution, the model makes no use of explicit preference heuristics
or revision strategies. Crucially, the structural decisions of the
model conform with human preferences, without those preferences having
been incorporated explicitly into the parser. Furthermore, the
competitive dynamics of the parsing network account for additional
on-line processing data that other models of syntactic preferences
have left unaddressed.
(Also cross-referenced as UMIACS-TR-95-55)