Using categorical grammars and a non-model-theoretic semantics to build automated representations of concepts: A non-keyterm approach to information retrieval.

Persistent Link:
http://hdl.handle.net/10150/187347
Title:
Using categorical grammars and a non-model-theoretic semantics to build automated representations of concepts: A non-keyterm approach to information retrieval.
Author:
Carlisle, Judith Pinn.
Issue Date:
1995
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:
This research develops an information retrieval system (IRS) using a semantic document representation derived using a combination of categorial grammars and conceptual semantics. Of particular interest to this research are documents in the document collection which a user would include in the set of retrieved documents, if the set was selected manually, yet are excluded by automated methods of IR. This research vigorously embraces the belief that language is a comprehensive system which encodes and transmits information. Therefore, traditional keyterm approaches used as a automatic document representation generation paradigm are rejected. Rather, natural language processing (NLP) and computational linguistic techniques are explored in depth as methods to automatically extract information from natural language texts and build electronic document representations. Important aspects of this project include implementation of an automated syntactic categorial parser and a conceptual semantic model to create a working IRS. Results of this research have proved promising and suggest that richer, more complex document representations can be applied to the automated information retrieval (IR) process.
Type:
text; Dissertation-Reproduction (electronic)
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Business Administration; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Purdin, Titus D. M.; Pingry, David E.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleUsing categorical grammars and a non-model-theoretic semantics to build automated representations of concepts: A non-keyterm approach to information retrieval.en_US
dc.creatorCarlisle, Judith Pinn.en_US
dc.contributor.authorCarlisle, Judith Pinn.en_US
dc.date.issued1995en_US
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.abstractThis research develops an information retrieval system (IRS) using a semantic document representation derived using a combination of categorial grammars and conceptual semantics. Of particular interest to this research are documents in the document collection which a user would include in the set of retrieved documents, if the set was selected manually, yet are excluded by automated methods of IR. This research vigorously embraces the belief that language is a comprehensive system which encodes and transmits information. Therefore, traditional keyterm approaches used as a automatic document representation generation paradigm are rejected. Rather, natural language processing (NLP) and computational linguistic techniques are explored in depth as methods to automatically extract information from natural language texts and build electronic document representations. Important aspects of this project include implementation of an automated syntactic categorial parser and a conceptual semantic model to create a working IRS. Results of this research have proved promising and suggest that richer, more complex document representations can be applied to the automated information retrieval (IR) process.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineBusiness Administrationen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairPurdin, Titus D. M.en_US
dc.contributor.chairPingry, David E.en_US
dc.contributor.committeememberVogel, Douglas R.en_US
dc.contributor.committeememberWeisband, Suzanne P.en_US
dc.contributor.committeememberOehrle, Richard T.en_US
dc.identifier.proquest9620407en_US
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