Simultaneously Acquiring the Syntax and Semantics of Spatial Referring Expressions

Persistent Link:
http://hdl.handle.net/10150/332831
Title:
Simultaneously Acquiring the Syntax and Semantics of Spatial Referring Expressions
Author:
Wright, Jeremy Bryan
Issue Date:
2014
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
To be useful for communication language must be grounded in perceptions of the world, but acquiring such grounded language is a challenging task that increases in difficulty as the length and syntactic complexity of utterances grow. Several state of the art methods exist to learn complex grounded language from unannotated utterances, however each requires that the semantic system of the language be completely defined ahead of time. This expectation is problematic as it assumes not only that agents must have complete semantic understanding before starting to learn language, but also that the human designers of these systems can accurately transcribe the semantics of human languages in great detail. This paper presents Reagent, a construction grammar framework for concurrently learning the syntax and semantics of complex English referring expressions, with an emphasis on spatial referring expressions. Rather than requiring fully predefined semantic representations, Reagent only requires access to a set of semantic primitives from which it can build appropriate representations. The results presented here demonstrate that Reagent can acquire constructions that are missing from its starting grammar by observing the contextual utterances of a fully fluent agent, can approach fluent accuracy at inferring the referent of such expressions, and learns meanings that are qualitatively similar to the constructions of the agent from which it is learning. We propose that this approach could be expanded to other types of expressions and languages, and forms a solid foundation for general natural language acquisition.
Type:
text; Electronic Dissertation
Keywords:
NLP; semantics; Computer Science; grounded
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Computer Science
Degree Grantor:
University of Arizona
Advisor:
Cohen, Paul

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleSimultaneously Acquiring the Syntax and Semantics of Spatial Referring Expressionsen_US
dc.creatorWright, Jeremy Bryanen_US
dc.contributor.authorWright, Jeremy Bryanen_US
dc.date.issued2014-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractTo be useful for communication language must be grounded in perceptions of the world, but acquiring such grounded language is a challenging task that increases in difficulty as the length and syntactic complexity of utterances grow. Several state of the art methods exist to learn complex grounded language from unannotated utterances, however each requires that the semantic system of the language be completely defined ahead of time. This expectation is problematic as it assumes not only that agents must have complete semantic understanding before starting to learn language, but also that the human designers of these systems can accurately transcribe the semantics of human languages in great detail. This paper presents Reagent, a construction grammar framework for concurrently learning the syntax and semantics of complex English referring expressions, with an emphasis on spatial referring expressions. Rather than requiring fully predefined semantic representations, Reagent only requires access to a set of semantic primitives from which it can build appropriate representations. The results presented here demonstrate that Reagent can acquire constructions that are missing from its starting grammar by observing the contextual utterances of a fully fluent agent, can approach fluent accuracy at inferring the referent of such expressions, and learns meanings that are qualitatively similar to the constructions of the agent from which it is learning. We propose that this approach could be expanded to other types of expressions and languages, and forms a solid foundation for general natural language acquisition.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectNLPen_US
dc.subjectsemanticsen_US
dc.subjectComputer Scienceen_US
dc.subjectgroundeden_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorCohen, Paulen_US
dc.contributor.committeememberSurdeanu, Mihaien_US
dc.contributor.committeememberBarnrd, Kobusen_US
dc.contributor.committeememberBeal, Caroleen_US
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