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Title:
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The Integration of Syntax and Semantic Plausibility in a Wide-Coverage Model of Human Sentence Processing
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Author:
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Ulrike Pado
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Email:
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click here to access email
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Homepage:
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http://www.nlpado.de/~ulrike
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Degree Awarded:
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Saarland University
, Department of Computational Linguistics and Phonetics
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Degree Date:
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2007
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Linguistic Subfield(s):
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Computational Linguistics
Psycholinguistics
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Director(s):
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Frank Keller
Matthew Crocker
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Abstract:
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Models of human sentence processing have paid much attention to three key
characteristics of the sentence processor: its robust and accurate
processing of unseen input (wide coverage), its immediate, incremental
interpretation of partial input and its sensitivity to structural
frequencies in previous language experience. In this thesis, we propose a
model of human sentence processing that accounts for these three
characteristics and also models a fourth key characteristic, namely the
influence of semantic plausibility on sentence processing.
The precondition for such a sentence processing model is a general model of
human plausibility intuitions. We therefore begin by presenting a
probabilistic model of the plausibility of verb-argument relations, which
we estimate as the probability of encountering a verb-argument pair in the
relation specified by a thematic role in a role-annotated training corpus.
This model faces a significant sparse data problem, which we alleviate by
combining two orthogonal smoothing methods. We show that the smoothed
model’s predictions are significantly correlated to human plausibility
judgements for a range of test sets. We also demonstrate that our semantic
plausibility model outperforms selectional preference models and a standard
role labeller, which solve tasks from computational linguistics that are
related to the prediction of human judgements.
We then integrate this semantic plausibility model with an incremental,
wide-coverage, probabilistic model of syntactic processing to form the
Syntax/Semantics (SynSem) Integration model of sentence processing. The
SynSem-Integration model combines preferences for candidate syntactic
structures from two sources: Syntactic probability estimates from a
probabilistic parser and our semantic plausibility model’s estimates of the
verb-argument relations in each syntactic analysis. The model uses these
preferences to determine a globally preferred structure and predicts
difficulty in human sentence processing either if syntactic and semantic
preferences conflict, or if the interpretation of the preferred analysis
changes non-monotonically. In a thorough evaluation against the patterns of
processing difficulty found for four ambiguity phenomena in eight
reading-time studies, we demonstrate that the SynSem-Integration model
reliably predicts human reading time behaviour.
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