大葉大學圖書館 |

Mining structures of factual knowledge from text :an effort-light approach /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	006.312
書名/作者:	Mining structures of factual knowledge from text : : an effort-light approach // Xiang Ren, Jiawei Han.
作者:	Ren, Xiang,
其他作者:	Han, Jiawei,
出版者:	[San Rafael, California] : : Morgan & Claypool,, 2018.
面頁冊數:	1 PDF (xv, 183 pages) : : illustrations.
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Electronic information resource searching.
標題:	Data mining.
標題:	Data structures (Computer science)
ISBN:	9781681733937
書目註:	Includes bibliographical references (pages 167-181).
內容註:	1. Introduction -- 1.1 Overview of the book -- 1.1.1 Part I: Identifying typed entities -- 1.1.2 Part II: Extracting typed entity relationships -- 1.1.3 Part III: Toward automated factual structure mining -- 2. Background -- 2.1 Entity structures -- 2.2 Relation structures -- 2.3 Distant supervision from knowledge bases -- 2.4 Mining entity and relation structures -- 2.5 Common notations -- 3. Literature review -- 3.1 Hand-crafted methods -- 3.2 Traditional supervised learning methods -- 3.2.1 Sequence labeling methods -- 3.2.2 Supervised relation extraction methods -- 3.3 Weakly supervised extraction methods -- 3.3.1 Semi-supervised learning -- 3.3.2 Pattern-based bootstrapping -- 3.4 Distantly supervised learning methods -- 3.5 Learning with noisy labeled data -- 3.6 Open-domain information extraction --
摘要、提要註:	The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including: (1) entity recognition, typing, and synonym discovery; (2) entity relation extraction; and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8424572

Mining structures of factual knowledge from text :an effort-light approach /
Ren, Xiang,

Mining structures of factual knowledge from text :an effort-light approach /Xiang Ren, Jiawei Han. - [San Rafael, California] :Morgan & Claypool,2018. - 1 PDF (xv, 183 pages) :illustrations. - Synthesis lectures on data mining and knowledge discovery,# 152151-0075 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 167-181).

1. Introduction -- 1.1 Overview of the book -- 1.1.1 Part I: Identifying typed entities -- 1.1.2 Part II: Extracting typed entity relationships -- 1.1.3 Part III: Toward automated factual structure mining -- 2. Background -- 2.1 Entity structures -- 2.2 Relation structures -- 2.3 Distant supervision from knowledge bases -- 2.4 Mining entity and relation structures -- 2.5 Common notations -- 3. Literature review -- 3.1 Hand-crafted methods -- 3.2 Traditional supervised learning methods -- 3.2.1 Sequence labeling methods -- 3.2.2 Supervised relation extraction methods -- 3.3 Weakly supervised extraction methods -- 3.3.1 Semi-supervised learning -- 3.3.2 Pattern-based bootstrapping -- 3.4 Distantly supervised learning methods -- 3.5 Learning with noisy labeled data -- 3.6 Open-domain information extraction --

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including: (1) entity recognition, typing, and synonym discovery; (2) entity relation extraction; and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.

Mode of access: World Wide Web.

ISBN: 9781681733937

Standard No.: 10.2200/S00860ED1V01Y201806DMK015doiSubjects--Topical Terms:

481198
Electronic information resource searching.
Subjects--Index Terms:

mining factual structures

LC Class. No.: QA76.9.D343 / R455 2018

Dewey Class. No.: 006.312

Mining structures of factual knowledge from text :an effort-light approach /
LDR:08440nmm 2200673 i 4500 001 509486
003 IEEE
005 20180802131531.0
006 m eo d
007 cr cn |||m|||a
008 210524s2018 caua foab 000 0 eng d
020 $a 9781681733937 $q ebook
020 $z 9781681733944 $q hardcover
020 $z 9781681733920 $q paperback
024 7 $a 10.2200/S00860ED1V01Y201806DMK015 $2 doi
035 $a (CaBNVSL)swl000408588
035 $a (OCoLC)1047603274
035 $a 8424572
040 $a CaBNVSL $b eng $e rda $c CaBNVSL $d CaBNVSL
050 4 $a QA76.9.D343 $b R455 2018
082 0 4 $a 006.312 $2 23
100 1 $a Ren, Xiang, $e author. $3 728968
245 1 0 $a Mining structures of factual knowledge from text : $b an effort-light approach / $c Xiang Ren, Jiawei Han.
260 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
300 $a 1 PDF (xv, 183 pages) : $b illustrations.
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on data mining and knowledge discovery, $x 2151-0075 ; $v # 15
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 167-181).
505 0 $a 1. Introduction -- 1.1 Overview of the book -- 1.1.1 Part I: Identifying typed entities -- 1.1.2 Part II: Extracting typed entity relationships -- 1.1.3 Part III: Toward automated factual structure mining -- 2. Background -- 2.1 Entity structures -- 2.2 Relation structures -- 2.3 Distant supervision from knowledge bases -- 2.4 Mining entity and relation structures -- 2.5 Common notations -- 3. Literature review -- 3.1 Hand-crafted methods -- 3.2 Traditional supervised learning methods -- 3.2.1 Sequence labeling methods -- 3.2.2 Supervised relation extraction methods -- 3.3 Weakly supervised extraction methods -- 3.3.1 Semi-supervised learning -- 3.3.2 Pattern-based bootstrapping -- 3.4 Distantly supervised learning methods -- 3.5 Learning with noisy labeled data -- 3.6 Open-domain information extraction --
505 8 $a Part I. Identifying typed entities -- 4. Entity recognition and typing with knowledge bases -- 4.1 Overview and motivation -- 4.2 Problem definition -- 4.3 Relation phrase-based graph construction -- 4.3.1 Candidate generation -- 4.3.2 Mention-name subgraph -- 4.3.3 Name-relation phrase subgraph -- 4.3.4 Mention correlation subgraph -- 4.4 Clustering-integrated type propagation on graphs -- 4.4.1 Seed mention generation -- 4.4.2 Relation phrase clustering -- 4.4.3 The joint optimization problem -- 4.4.4 The ClusType algorithm -- 4.4.5 Computational complexity analysis -- 4.5 Experiments -- 4.5.1 Data preparation -- 4.5.2 Experimental settings -- 4.5.3 Experiments and performance study -- 4.6 Discussion -- 4.7 Summary -- 5. Fine-grained entity typing with knowledge bases -- 5.1 Overview and motivation -- 5.2 Preliminaries -- 5.3 The AFET framework -- 5.3.1 Text feature generation -- 5.3.2 Training set partition -- 5.3.3 The joint mention-type model -- 5.3.4 Modeling type correlation -- 5.3.5 Modeling noisy type labels -- 5.3.6 Hierarchical partial-label embedding -- 5.4 Experiments -- 5.4.1 Data preparation -- 5.4.2 Evaluation settings -- 5.4.3 Performance comparison and analyses -- 5.5 Discussion and case analysis -- 5.6 Summary -- 6. Synonym discovery from large corpus / Meng Qu -- 6.1 Overview and motivation -- 6.1.1 Challenges -- 6.1.2 Proposed solution -- 6.2 The DPE framework -- 6.2.1 Synonym seed collection -- 6.2.2 Joint optimization problem -- 6.2.3 Distributional module -- 6.2.4 Pattern module -- 6.3 Experiment -- 6.4 Summary --
505 8 $a Part II. Extracting typed relationships -- 7. Joint extraction of typed entities and relationships -- 7.1 Overview and motivation -- 7.2 Preliminaries -- 7.3 The CoType framework -- 7.3.1 Candidate generation -- 7.3.2 Joint entity and relation embedding -- 7.3.3 Model learning and type inference -- 7.4 Experiments -- 7.4.1 Data preparation and experiment setting -- 7.4.2 Experiments and performance study -- 7.5 Discussion -- 7.6 Summary -- 8. Pattern-enhanced embedding learning for relation extraction / Meng Qu -- 8.1 Overview and motivation -- 8.1.1 Challenges -- 8.1.2 Proposed solution -- 8.2 The REPEL framework -- 8.3 Experiment -- 8.4 Summary -- 9. Heterogeneous supervision for relation extraction / Liyuan Liu -- 9.1 Overview and motivation -- 9.2 Preliminaries -- 9.2.1 Relation extraction -- 9.2.2 Heterogeneous supervision -- 9.2.3 Problem definition -- 9.3 The REHession framework -- 9.3.1 Modeling relation mention -- 9.3.2 True label discovery -- 9.3.3 Modeling relation type -- 9.3.4 Model learning -- 9.3.5 Relation type inference -- 9.4 Experiments -- 9.5 Summary -- 10. Indirect supervision: leveraging knowledge from auxiliary tasks / Zeqiu Wu -- 10.1 Overview and motivation -- 10.1.1 Challenges -- 10.1.2 Proposed solution -- 10.2 The proposed approach -- 10.2.1 Heterogeneous network construction -- 10.2.2 Joint RE and QA embedding -- 10.2.3 Type inference -- 10.3 Experiments -- 10.4 Summary --
505 8 $a Part III. Toward automated factual structure mining -- 11. Mining entity attribute values with meta patterns / Meng Jiang -- 11.1 Overview and motivation -- 11.1.1 Challenges -- 11.1.2 Proposed solution -- 11.1.3 Problem formulation -- 11.2 The MetaPAD framework -- 11.2.1 Generating meta patterns by context-aware segmentation -- 11.2.2 Grouping synonymous meta patterns -- 11.2.3 Adjusting type levels for preciseness -- 11.3 Summary -- 12. Open information extraction with global structure cohesiveness / Qi Zhu -- 12.1 Overview and motivation -- 12.1.1 Proposed solution -- 12.2 The ReMine framework -- 12.2.1 The joint optimization problem -- 12.3 Summary -- 13. Applications -- 13.1 Structuring life science papers: the Life-iNet system -- 13.2 Extracting document facets from technical corpora -- 13.3 Comparative document analysis -- 14. Conclusions -- 14.1 Effort-light StructMine: summary -- 14.2 Conclusion -- 15. Vision and future work -- 15.1 Extracting implicit patterns from massive unlabeled corpora -- 15.2 Enriching factual structure representation --
505 8 $a Bibliography -- Authors' biographies.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including: (1) entity recognition, typing, and synonym discovery; (2) entity relation extraction; and (3) open-domain attribute-value mining and information extraction. This book introduces this new research frontier and points out some promising research directions.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on August 1, 2018).
650 0 $a Electronic information resource searching. $3 481198
650 0 $a Data mining. $3 337740
650 0 $a Data structures (Computer science) $3 417983
653 $a mining factual structures
653 $a information extraction
653 $a knowledge bases
653 $a entity recognition and typing
653 $a relation extraction
653 $a entity synonym mining
653 $a distant supervision
653 $a effort-light approach
653 $a classification
653 $a clustering
653 $a real-world applications
653 $a scalable algorithms
700 1 $a Han, Jiawei, $e author. $3 728969
776 0 8 $i Print version: $z 9781681733920 $z 9781681733944
830 0 $a Synthesis digital library of engineering and computer science. $3 461208
830 0 $a Synthesis lectures on data mining and knowledge discovery ; $v # 15. $x 2151-0075 $3 728970
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8424572