000 02217nam a22002895i 4500
999 _c28239
_d28210
001 20306424
003 EG-ScBUE
005 20200305144639.0
008 180125t20182018sz a f b 001 0 eng d
020 _a9783319735306
040 _aDLC
_beng
_erda
_cDLC
_dEG-ScBUE
082 0 4 _a006.31
_bAGG
_222
100 1 _aAggarwal, Charu C.,
_eauthor.
245 1 0 _aMachine learning for text /
_cCharu C. Aggarwal.
264 1 _aCham, Switzerland :
_bSpringer / Springer International Publishing,
_c[2018]
264 4 _cc2018
300 _axxiii, 493 pages :
_billustrations ;
_c28 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
520 _aText analytics is a field that lies on the interface of information retrieval, machine learning, and natural language processing. This book carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 8 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection. This book covers text analytics and machine learning topics from the simple to the advanced. Since the coverage is extensive, multiple courses can be offered from the same book, depending on course level.
650 7 _aMachine learning.
_2BUEsh
650 7 _aText processing (Computer science)
_2BUEsh
_97933
653 _bCOMSCI
_cMarch2020
942 _2ddc
_cBB