000 04469nam a22005295i 4500
001 978-3-031-01914-2
003 DE-He213
005 20240730163748.0
007 cr nn 008mamaa
008 220601s2019 sz | s |||| 0|eng d
020 _a9783031019142
_9978-3-031-01914-2
024 7 _a10.1007/978-3-031-01914-2
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aZhang, Chao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980435
245 1 0 _aMultidimensional Mining of Massive Text Data
_h[electronic resource] /
_cby Chao Zhang, Jiawei Han.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIII, 183 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_980436
650 2 4 _aStatistics.
_914134
700 1 _aHan, Jiawei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_92109
710 2 _aSpringerLink (Online service)
_980437
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001093
776 0 8 _iPrinted edition:
_z9783031007866
776 0 8 _iPrinted edition:
_z9783031030420
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_980438
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01914-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c84961
_d84961