LEADER 03644nam 22005295 450 001 9910299459603321 005 20200705065051.0 010 $a3-319-73531-4 024 7 $a10.1007/978-3-319-73531-3 035 $a(CKB)4100000002892262 035 $a(DE-He213)978-3-319-73531-3 035 $a(MiAaPQ)EBC6314099 035 $a(MiAaPQ)EBC5589130 035 $a(Au-PeEL)EBL5589130 035 $a(OCoLC)1029870455 035 $a(PPN)225553392 035 $a(EXLCZ)994100000002892262 100 $a20180319d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine Learning for Text /$fby Charu C. Aggarwal 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (XXIII, 493 p. 80 illus., 4 illus. in color.) 311 $a3-319-73530-6 327 $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 Text Sequence Modeling and Deep Learning -- 11 Text Summarization -- 12 Information Extraction -- 13 Opinion Mining and Sentiment Analysis -- 14 Text Segmentation and Event Detection. 330 $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. 606 $aData mining 606 $aArtificial intelligence 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a006.31 700 $aAggarwal$b Charu C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299459603321 996 $aMachine Learning for Text$92175811 997 $aUNINA