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|Title: ||Broad-coverage model of prediction in human sentence processing|
|Authors: ||Demberg-Winterfors, Vera|
|Supervisor(s): ||Keller, Frank|
|Issue Date: ||2010|
|Publisher: ||The University of Edinburgh|
|Abstract: ||The aim of this thesis is to design and implement a cognitively plausible theory
of sentence processing which incorporates a mechanism for modeling a prediction
and verification process in human language understanding, and to evaluate the validity
of this model on specific psycholinguistic phenomena as well as on broad-coverage,
naturally occurring text.
Modeling prediction is a timely and relevant contribution to the field because recent
experimental evidence suggests that humans predict upcoming structure or lexemes
during sentence processing. However, none of the current sentence processing theories
capture prediction explicitly. This thesis proposes a novel model of incremental
sentence processing that offers an explicit prediction and verification mechanism.
In evaluating the proposed model, this thesis also makes a methodological contribution.
The design and evaluation of current sentence processing theories are usually
based exclusively on experimental results from individual psycholinguistic experiments
on specific linguistic structures. However, a theory of language processing in
humans should not only work in an experimentally designed environment, but should
also have explanatory power for naturally occurring language.
This thesis first shows that the Dundee corpus, an eye-tracking corpus of newspaper
text, constitutes a valuable additional resource for testing sentence processing theories.
I demonstrate that a benchmark processing effect (the subject/object relative clause
asymmetry) can be detected in this data set (Chapter 4). I then evaluate two existing
theories of sentence processing, Surprisal and Dependency Locality Theory (DLT),
on the full Dundee corpus. This constitutes the first broad-coverage comparison of
sentence processing theories on naturalistic text. I find that both theories can explain
some of the variance in the eye-movement data, and that they capture different aspects
of sentence processing (Chapter 5).
In Chapter 6, I propose a new theory of sentence processing, which explicitly models
prediction and verification processes, and aims to unify the complementary aspects
of Surprisal and DLT. The proposed theory implements key cognitive concepts such
as incrementality, full connectedness, and memory decay. The underlying grammar
formalism is a strictly incremental version of Tree-adjoining Grammar (TAG), Psycholinguistically
motivated TAG (PLTAG), which is introduced in Chapter 7. I then
describe how the Penn Treebank can be converted into PLTAG format and define an
incremental, fully connected broad-coverage parsing algorithm with associated probability
model for PLTAG. Evaluation of the PLTAG model shows that it achieves the broad coverage required for testing a psycholinguistic theory on naturalistic data. On
the standardized Penn Treebank test set, it approaches the performance of incremental
TAG parsers without prediction (Chapter 8).
Chapter 9 evaluates the psycholinguistic aspects of the proposed theory by testing
it both on a on a selection of established sentence processing phenomena and on the
Dundee eye-tracking corpus. The proposed theory can account for a larger range of
psycholinguistic case studies than previous theories, and is a significant positive predictor
of reading times on broad-coverage text. I show that it can explain a larger
proportion of the variance in reading times than either DLT integration cost or Surprisal.|
|Sponsor(s): ||Engineering and Physical Sciences Research Council (EPSRC)|
human sentence processing
|Appears in Collections:||Informatics thesis and dissertation collection|
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