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020 _a9783030966232
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024 7 _a10.1007/978-3-030-96623-2
_2doi
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072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
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082 0 4 _a006.31
_223
100 1 _aAggarwal, Charu C.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning for Text
_h[electronic resource] /
_cby Charu C. Aggarwal.
250 _a2nd ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXXIII, 565 p. 92 illus., 5 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _a1 An Introduction to Text Analytics -- 2 Text Preparation and Similarity Computation -- 3 Matrix Factorization and Topic Modeling -- 4 Text Clustering -- 5 Text Classification: Basic Models -- 6 Linear Models for Classification and Regression -- 7 Classifier Performance and Evaluation -- 8 Joint Text Mining with Heterogeneous Data -- 9 Information Retrieval and Search Engines -- 10 Language Modeling and Deep Learning -- 11 Attention Mechanisms and Transformers -- 12 Text Summarization -- 13 Information Extraction and Knowledge Graphs -- 14 Question Answering -- 15 Opinion Mining and Sentiment Analysis -- 16 Text Segmentation and Event Detection.
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.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aMachine Learning.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aInformation Storage and Retrieval.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030966225
776 0 8 _iPrinted edition:
_z9783030966249
776 0 8 _iPrinted edition:
_z9783030966256
856 4 0 _uhttps://doi.org/10.1007/978-3-030-96623-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178990
_d178990