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008			220601s2019 sz | s |||| 0|eng d
020			_a9783031019142
024	7		_a10.1007/978-3-031-01914-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.D343 _b2019 EB
100	1		_aZhang, Chao _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687234
245	1	0	_aMultidimensional Mining of Massive Text Data _cby Chao Zhang, Jiawei Han
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIII, 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		_aIntroduction -- Topic-Level Taxonomy Generation -- Term-Level Taxonomy Generation -- Weakly Supervised Text Classification -- Weakly Supervised Hierarchical Text Classification -- Multidimensional Summarization -- Cross-Dimension Prediction in Cube Space -- Event Detection in Cube Space -- Conclusions -- Bibliography -- Authors' Biographies.
520			_aUnstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people's information need from text data is becoming multidimensional-they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task. This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling from user-selected data multidimensional knowledge. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multidimensional, multigranular data selection with declarative queries; and with cube exploitation algorithms, users can extract multidimensional patterns from the selected data for decision making. The proposed framework has two distinctive advantages when turning text data into multidimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multidimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multidimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9141188 _aProceso de textos
700	1		_aHan, Jiawei _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686325
776	0	8	_iPrinted edition: _z9783031001093
776	0	8	_iPrinted edition: _z9783031007866
776	0	8	_iPrinted edition: _z9783031030420
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01914-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI