An expectation model of referring expressions
Name
707634076-MIT.pdf
Description
Full printable version
Size
13.17 MB
Format
Adobe PDF
Checksum (MD5)
e87ad78126fcc3eeefbb0e5c4a6a0e42
Author(s)
Kræmer, John, Ph. D. Massachusetts Institute of Technology
Advisor(s)
Edward Gibson.
Alternative Title
EMRE
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
This thesis introduces EMRE, an expectation-based model of referring expressions. EMRE is proposed as a model of non-syntactic dependencies - in particular, discourse-level semantic dependencies that bridge sentence gaps. These include but are not limited to anaphora (references to noun phrases in previous sentences) and coherence predicates such as causality, temporal ordering and resemblance -- two domains that have typically been treated as entirely distinct aspects of language. EMRE is a computational-level model, and is agnostic about any particular algorithms, cognitive faculties, or neurological substrates that might be applied to the problem of semantic reference. Instead, it describes reference as a computational problem framed in terms of expectation and inference, and describes a solution to the problem based on rational top-down expectations about the likely targets of referring expressions, and on bottom-up feature-based matching that occurs when a referring expression is encountered. EMRE is used to derive novel empirical predictions about how people will construe particular discourse constructions involving NP anaphora and coherence predicates. These predictions are tested in controlled behavioral experiments, in which participants read and answer questions about short texts. The results of these experiments are shown to be consistent with a model of reference as an expectation-based computational structure with different underlying rules than those governing syntactic processing.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 297-205).
Subjects
Brain and Cognitive Sciences.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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