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Commitment-based learning of hidden linguistic structures
Descriptive
TitleInfo
Title
Commitment-based learning of hidden linguistic structures
Name
(type = personal)
NamePart
(type = family)
Akers
NamePart
(type = given)
Crystal Gayle
NamePart
(type = date)
1978-
DisplayForm
Crystal Akers
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Tesar
NamePart
(type = given)
Bruce
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Bruce Tesar
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Prince
NamePart
(type = given)
Alan
DisplayForm
Alan Prince
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Kawahara
NamePart
(type = given)
Shigeto
DisplayForm
Shigeto Kawahara
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Jarosz
NamePart
(type = given)
Gaja
DisplayForm
Gaja Jarosz
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = corporate)
NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
(type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm
(authority = RULIB)
school
TypeOfResource
Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(qualifier = exact)
2012
DateOther
(qualifier = exact);
(type = degree)
2012-05
CopyrightDate
(qualifier = exact)
2012
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Learners must simultaneously learn a grammar and a lexicon from observed forms, yet some structures that the grammar and lexicon reference are unobservable in the acoustic signal. Moreover, these “hidden” structures interact: the grammar maps an underlying form to a particular interpretation. Learning one structure depends on learning the structures it interacts with, but if the learner commits to one structure, its interactions can be exploited to learn others. The Commitment-Based Learner (CBL) employs this strategy using error-driven learning (Gold 1967, Wexler and Culicover 1980) and inconsistency detection (Tesar 1997) to determine when to make commitments and what kinds of commitments to make. The CBL overcomes structural ambiguity by extending branches from a hypothesis and committing to a separate structural interpretation in each branch, as in the Inconsistency Detection Learner (Tesar 2004). It resolves lexical ambiguity by making piecewise commitments to feature values, following the Output-Driven Learner (Tesar, to appear). Each branch has its own lexicon whose values reflect the interactions of underlying forms with the branch’s structural commitments. In computer simulations, the CBL learns all 97 languages in a constructed typology whose linguistic system includes 370 million grammar and lexicon combinations. For each language learned, the CBL takes far fewer steps than needed to exhaustively search for a consistent and restrictive combination. Employing inconsistency detection with Multi-Recursive Constraint Demotion (Tesar 1997) makes the CBL highly efficient, and it compares favorably in success and efficiency to its major stochastic competitors (Apoussidou 2007, Jarosz 2006, to appear). The dissertation also introduces a previously unrecognized global lexical ambiguity defined by paradigmatic equality. Paradigmatic equals (PEs) have different grammars, but because their morpheme behaviors are identical, their learning data are equivalent and foil learning by inconsistency detection. To distinguish PEs, the CBL finds consistent mappings derived from words with unset features set to mismatch their surface values. A mapping with an error by the current ranking contributes new ranking information, allowing the learner to derive the hypothesis consistent with the PE that includes the mapping. In the system investigated, there are always two such mappings, each corresponding to a different PE.
Subject
(authority = RUETD)
Topic
Linguistics
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_3936
PhysicalDescription
Form
(authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
ix, 436 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Crystal Gayle Akers
Subject
(authority = ETD-LCSH)
Topic
Linguistics--Computer programs
Subject
(authority = ETD-LCSH)
Topic
Language and languages--Grammars
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000065071
RelatedItem
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T32V2F3T
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Akers
GivenName
Crystal
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
(encoding = w3cdtf);
(qualifier = exact);
(point = start)
2012-04-12 21:20:15
AssociatedEntity
Name
Crystal Akers
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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