Unsupervised Information Extraction by Text Segmentation (Record no. 96604)

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001 - CONTROL NUMBER
control field 978-3-319-02597-1
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220082841.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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fixed length control field 131023s2013 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319025971
-- 978-3-319-02597-1
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-319-02597-1
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D3
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UMT
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM021000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Cortez, Eli.
Relator term author.
245 10 - TITLE STATEMENT
Title Unsupervised Information Extraction by Text Segmentation
Medium [electronic resource] /
Statement of responsibility, etc by Eli Cortez, Altigran S. Silva.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2013.
300 ## - PHYSICAL DESCRIPTION
Extent XV, 94 p. 25 illus.
Other physical details online resource.
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-- text
-- txt
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-- computer
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-- rdamedia
338 ## -
-- online resource
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347 ## -
-- text file
-- PDF
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490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Computer Science,
International Standard Serial Number 2191-5768
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Foreword -- Preface -- Introduction -- Related Work -- Exploiting Pre-Existing Datasets to Support IETS -- ONDUX -- JUDIE -- iForm -- Conclusions and Future Work.
520 ## - SUMMARY, ETC.
Summary, etc A new unsupervised approach to the problem of Information Extraction by Text Segmentation (IETS) is proposed, implemented and evaluated herein. The authors’ approach relies on information available on pre-existing data to learn how to associate segments in the input string with attributes of a given domain relying on a very effective set of content-based features. The effectiveness of the content-based features is also exploited to directly learn from test data structure-based features, with no previous human-driven training, a feature unique to the presented approach. Based on the approach, a number of results are produced to address the IETS problem in an unsupervised fashion. In particular, the authors develop, implement and evaluate distinct IETS methods, namely ONDUX, JUDIE and iForm. ONDUX (On Demand Unsupervised Information Extraction) is an unsupervised probabilistic approach for IETS that relies on content-based features to bootstrap the learning of structure-based features. JUDIE (Joint Unsupervised Structure Discovery and Information Extraction) aims at automatically extracting several semi-structured data records in the form of continuous text and having no explicit delimiters between them. In comparison with other IETS methods, including ONDUX, JUDIE faces a task considerably harder, that is, extracting information while simultaneously uncovering the underlying structure of the implicit records containing it. iForm applies the authors’ approach to the task of Web form filling. It aims at extracting segments from a data-rich text given as input and associating these segments with fields from a target Web form. All of these methods were evaluated considering different experimental datasets, which are used to perform a large set of experiments in order to validate the presented approach and methods. These experiments indicate that the proposed approach yields high quality results when compared to state-of-the-art approaches and that it is able to properly support IETS methods in a number of real applications. The findings will prove valuable to practitioners in helping them to understand the current state-of-the-art in unsupervised information extraction techniques, as well as to graduate and undergraduate students of web data management.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Database management.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information storage and retrieval systems.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Database Management.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information Storage and Retrieval.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Silva, Altigran S.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783319025964
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title SpringerBriefs in Computer Science,
-- 2191-5768
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-02597-1
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