Meaning and compositionality as statistical induction of categories and constraints
Name
601820947-MIT.pdf
Description
Full printable version
Size
19.51 MB
Format
Adobe PDF
Checksum (MD5)
8e521394c07d29a436a462c7a6fe1964
Author(s)
Schmidt, Lauren A
Advisor(s)
Joshua Tenenbaum.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
What do words and phrases mean? How do we infer their meaning in a given context? How do we know which sets of words have sensible meanings when combined, as opposed to being nonsense? As language learners and speakers, we can solve these problems starting at a young age, but as scientists, our understanding of these processes is limited. This thesis seeks to address these questions using a computational approach. Bayesian modeling provides a method of combining categories and logical constraints with probabilistic inference, yielding word and phrase meanings that involve graded category memberships and are governed by probabilistically inferred structures. The Bayesian approach also allows an investigation to separately identify the prior beliefs a language user brings to a particular situation involving meaning-based inference (e.g., learning a word meaning or identifying which objects an adjective applies to within a given context), and to identify what the language user can infer from the context. This approach therefore provides the foundation also for investigations of how different prior beliefs affect what a language user infers in a given situation, and how prior beliefs can develop over time. Using a computational approach, I address the following questions: (1) How do people generalize about a word's meaning from limited evidence? (2) How do people understand and use phrases, particularly when some of the words in those phrases depend on context for interpretation? (3) How do people know and learn which combinations of predicates and noun phrases can sensibly be combined and which are nonsensical?
(cont.) I show how each of these topics involves the probabilistic induction of categories, and I examine the constraints on inference in each domain. I also explore which of these constraints may themselves be learned.
Description
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2009.
"September 2009." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 191-201).
Subjects
Brain and Cognitive Sciences.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
Persistent DSpace Link
