000			02640cam a22003015i 4500
008			180319s2018 gw a o 000 0 eng
020			_a9783030966225 _qhardback _cRM372.93
040			_aDLC _beng _epn _erda _cDLC _dUKM _erda
090			_aQ325.5 _b.A354 2022 3
100	1		_aAggarwal, Charu C, _eauthor.
245	1	0	_aMachine learning for text / _cCharu C. Aggarwal.
250			_aSecond edition.
264		1	_aCham, Switzerland : _bSpringer, _c2022.
300			_axxiii, 565 pages _billustrations _c26cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
504			_aIncludes bibliographical references and index
520			_aThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories: 1. Basic algorithms: Chapters 1 through 7 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 and information retrieval: 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. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering
650		0	_aMachine learning.
650		0	_aData mining.
907			_a.b17004974 _b2024-03-18 _c2023-10-02
942			_c01 _n0 _kQ325.5 .A354 2022 3
949			_o600000892
990			_azsz
991			_aFakulti Sains Teknologi dan Maklumat
998			_al _b2023-10-02 _cm _da _feng _ggw _y0 _z.b17004974
999			_c667267 _d667267