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008			220601s2018 sz | o |||| 0|eng d
020			_a9783031019128
024	7		_a10.1007/978-3-031-01912-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2018 EB
100	1		_aRen, Xiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686324
245	1	0	_aMining Structures of Factual Knowledge from Text : _bAn Effort-Light Approach _cby Xiang Ren, Jiawei Han
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XV, 183 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Mining and Knowledge Discovery _x2151-0075
505	0		_aAcknowledgments -- Introduction -- Background -- Literature Review -- Entity Recognition and Typing with Knowledge Bases -- Fine-Grained Entity Typing with Knowledge Bases -- Synonym Discovery from Large Corpus -- Joint Extraction of Typed Entities and Relationships -- Pattern-Enhanced Embedding Learning for Relation Extraction -- Heterogeneous Supervision for Relation Extraction -- Indirect Supervision: Leveraging Knowledge from Auxiliary Tasks -- Mining Entity Attribute Values with Meta Patterns -- Open Information Extraction with Global Structure Cohesiveness -- Open Information Extraction with Global Structure Cohesiveness -- Applications -- Conclusions -- Vision and Future Work -- Bibliography -- Authors' Biographies.
520			_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.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9162648 _aData mining
700	1		_aHan, Jiawei _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686325
776	0	8	_iPrinted edition: _z9783031001079
776	0	8	_iPrinted edition: _z9783031007842
776	0	8	_iPrinted edition: _z9783031030406
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01912-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI