| 000 | 08804nam a22007571i 4500 | ||
|---|---|---|---|
| 001 | 8424572 | ||
| 003 | IEEE | ||
| 005 | 20200413152926.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 180801s2018 caua foab 000 0 eng d | ||
| 020 |
_a9781681733937 _qebook |
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| 020 |
_z9781681733944 _qhardcover |
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| 020 |
_z9781681733920 _qpaperback |
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| 024 | 7 |
_a10.2200/S00860ED1V01Y201806DMK015 _2doi |
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| 035 | _a(CaBNVSL)swl000408588 | ||
| 035 | _a(OCoLC)1047603274 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQA76.9.D343 _bR455 2018 |
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| 082 | 0 | 4 |
_a006.312 _223 |
| 100 | 1 |
_aRen, Xiang, _eauthor. |
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| 245 | 1 | 0 |
_aMining structures of factual knowledge from text : _ban effort-light approach / _cXiang Ren, Jiawei Han. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2018. |
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| 300 |
_a1 PDF (xv, 183 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on data mining and knowledge discovery, _x2151-0075 ; _v# 15 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 167-181). | ||
| 505 | 0 | _a1. 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 | _aPart 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 | _aPart 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 | _aPart 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 | _aBibliography -- Authors' biographies. | |
| 506 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aThe 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 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on August 1, 2018). | ||
| 650 | 0 | _aElectronic information resource searching. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aData structures (Computer science) | |
| 653 | _amining factual structures | ||
| 653 | _ainformation extraction | ||
| 653 | _aknowledge bases | ||
| 653 | _aentity recognition and typing | ||
| 653 | _arelation extraction | ||
| 653 | _aentity synonym mining | ||
| 653 | _adistant supervision | ||
| 653 | _aeffort-light approach | ||
| 653 | _aclassification | ||
| 653 | _aclustering | ||
| 653 | _areal-world applications | ||
| 653 | _ascalable algorithms | ||
| 700 | 1 |
_aHan, Jiawei, _eauthor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781681733920 _z9781681733944 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on data mining and knowledge discovery ; _v# 15. _x2151-0075 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8424572 |
| 999 |
_c562307 _d562307 |
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