000 06511nam a22007091i 4500
001 8673866
003 IEEE
005 20200413152931.0
006 m eo d
007 cr cn |||m|||a
008 190402s2019 caua foab 000 0 eng d
020 _a9781681735207
_qelectronic
020 _z9781681735214
_qhardcover
020 _z9781681735191
_qpaperback
024 7 _a10.2200/S00903ED1V01Y201902DMK017
_2doi
035 _a(CaBNVSL)thg00978686
035 _a(OCoLC)1091193939
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQA76.9.D343
_bZ536 2019eb
082 0 4 _a006.312
_223
100 1 _aZhang, Chao
_c(Computer scientist),
_eauthor.
245 1 0 _aMultidimensional mining of massive text data /
_cChao Zhang, Jiawei Han.
264 1 _a[San Rafael, California] :
_bMorgan & Claypool,
_c[2019]
300 _a1 PDF (xiv, pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aSynthesis lectures on data mining and knowledge discovery,
_x2151-0067 ;
_v#17
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 169-181).
505 0 _a1. Introduction -- 1.1. Overview -- 1.2. Main parts -- 1.3. Technical roadmap -- 1.4. Organization
505 8 _apart I. Cube construction algorithms. 2. Topic-level taxonomy generation -- 2.1. Overview -- 2.2. Related work -- 2.3. Preliminaries -- 2.4. Adaptive term clustering -- 2.5. Adaptive term embedding -- 2.6. Experimental evaluation -- 2.7. Summary
505 8 _a3. Term-level taxonomy generation / Jiaming Shen -- 3.1. Overview -- 3.2. Related work -- 3.3. Problem formulation -- 3.4. The HiExpan framework -- 3.5. Experiments -- 3.6. Summary
505 8 _a4. Weakly supervised text classification / Yu Meng -- 4.1. Overview -- 4.2. Related work -- 4.3. Preliminaries -- 4.4. Pseudo-document generation -- 4.5. Neural models with self-training -- 4.6. Experiments -- 4.7. Summary 69
505 8 _a5. Weakly supervised hierarchical text classification / Yu Meng -- 5.1. Overview -- 5.2. Related work -- 5.3. Problem formulation -- 5.4. Pseudo-document generation -- 5.5. The hierarchical classification model -- 5.6. Experiments -- 5.7. Summary
505 8 _apart II. Cube exploitation algorithms. 6. Multidimensional summarization / Fangbo Tao -- 6.1. Introduction -- 6.2. Related work -- 6.3. Preliminaries -- 6.4. The ranking measure -- 6.5. The RepPhrase method -- 6.6. Experiments -- 6.7. Summary
505 8 _a7. Cross-dimension prediction in cube space -- 7.1. Overview -- 7.2. Related work -- 7.3. Preliminaries -- 7.4. Semi-supervised multimodal embedding -- 7.5. Online updating of multimodal embedding -- 7.6. Experiments -- 7.7. Summary
505 8 _a8. Event detection in cube space -- 8.1. Overview -- 8.2. Related work -- 8.3. Preliminaries -- 8.4. Candidate generation -- 8.5. Candidate classification -- 8.6. Supporting continuous event detection -- 8.7. Complexity analysis -- 8.8. Experiments -- 8.9. Summary
505 8 _a9. Conclusions -- 9.1. Summary -- 9.2. Future work.
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 _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.
530 _aAlso available in print.
588 _aTitle from PDF title page (viewed on April 2, 2019).
650 0 _aData mining.
650 0 _aText processing (Computer science)
653 _atext mining
653 _amultidimensional analysis
653 _adata cube
653 _alimited supervision
700 1 _aHan, Jiawei,
_eauthor.
776 0 8 _iPrint version:
_z9781681735214
_z9781681735191
830 0 _aSynthesis digital library of engineering and computer science.
830 0 _aSynthesis lectures on data mining and knowledge discovery ;
_v#17.
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8673866
856 4 0 _3Abstract with links to full text
_uhttps://doi.org/10.2200/S00903ED1V01Y201902DMK017
999 _c562392
_d562392