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Addressing information proliferation...

Li, Jingjing.

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Addressing information proliferation: Applications of information extraction and text mining.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Addressing information proliferation: Applications of information extraction and text mining./
Author:	Li, Jingjing.
Description:	168 p.
Notes:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: A.
Contained By:	Dissertation Abstracts International74-09A(E).
Subject:	Business Administration, General. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3561998
ISBN:	9781303094095

Addressing information proliferation: Applications of information extraction and text mining.
Li, Jingjing.

Addressing information proliferation: Applications of information extraction and text mining. - 168 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: A.

Thesis (Ph.D.)--University of Colorado at Boulder, 2013.

The advent of the Internet and the ever-increasing capacity of storage media have made it easy to store, deliver, and share enormous volumes of data, leading to a proliferation of information on the Web, in online libraries, on news wires, and almost everywhere in our daily lives. Since our ability to process and absorb this information remains relatively constant, there is an imperative demand for novel tools to help explore, extract, and understand this information. Information extraction and text mining are two research endeavors that seek to extract structured information and discover knowledge patterns from unstructured text data. Based on state-of-the-art information extraction and text mining techniques, this dissertation presents three essays that address the information proliferation in both academia and industry. Specifically, the first two essays focus on extracting constructs, theoretical models, and theory-specific citation patterns for the behavioral sciences, and the last essay aims to build a high-quality recommendation engine for the movie industry by combining textual and numerical information to create personalized output. The evaluation results for the system performance represent a promising opportunity to apply information extraction and text mining to the business domain.

ISBN: 9781303094095Subjects--Topical Terms:

1017457
Business Administration, General.

Addressing information proliferation: Applications of information extraction and text mining.
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100 1 $a Li, Jingjing. $3 1285280
245 1 0 $a Addressing information proliferation: Applications of information extraction and text mining.
300 $a 168 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: A.
500 $a Adviser: Kai R. Larsen.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2013.
520 $a The advent of the Internet and the ever-increasing capacity of storage media have made it easy to store, deliver, and share enormous volumes of data, leading to a proliferation of information on the Web, in online libraries, on news wires, and almost everywhere in our daily lives. Since our ability to process and absorb this information remains relatively constant, there is an imperative demand for novel tools to help explore, extract, and understand this information. Information extraction and text mining are two research endeavors that seek to extract structured information and discover knowledge patterns from unstructured text data. Based on state-of-the-art information extraction and text mining techniques, this dissertation presents three essays that address the information proliferation in both academia and industry. Specifically, the first two essays focus on extracting constructs, theoretical models, and theory-specific citation patterns for the behavioral sciences, and the last essay aims to build a high-quality recommendation engine for the movie industry by combining textual and numerical information to create personalized output. The evaluation results for the system performance represent a promising opportunity to apply information extraction and text mining to the business domain.
590 $a School code: 0051.
650 4 $a Business Administration, General. $3 1017457
650 4 $a Information Technology. $3 1030799
650 4 $a Information Science. $3 1017528
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710 2 $a University of Colorado at Boulder. $b Business. $3 1018464
773 0 $t Dissertation Abstracts International $g 74-09A(E).
790 1 0 $a Larsen, Kai R., $e advisor
790 1 0 $a Kozar, Kenneth A. $e committee member
790 1 0 $a Lee, Jintae $e committee member
790 1 0 $a Hovorka, Dirk S. $e committee member
790 1 0 $a Nicolov, Nicolas $e committee member
790 $a 0051
791 $a Ph.D.
792 $a 2013
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3561998