LEADER 05680nam 2200565 450 001 9910811011803321 005 20231110213118.0 010 $a1-00-333822-4 010 $a1-003-33822-4 010 $a1-000-79358-3 010 $a87-7022-342-4 035 $a(CKB)4100000011811878 035 $a(MiAaPQ)EBC6526869 035 $a(Au-PeEL)EBL6526869 035 $a(OCoLC)1244622420 035 $a(MiAaPQ)EBC30251747 035 $a(Au-PeEL)EBL30251747 035 $a(NjHacI)994100000011811878 035 $a(EXLCZ)994100000011811878 100 $a20230213d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aFunctional applications of text analytics systems /$fSteven Simske and Marie Vans, editors 205 $a1st ed. 210 1$aDenmark :$cRiver Publishers,$d[2021] 210 4$dİ2021 215 $a1 online resource (292 pages) 225 0 $aRiver Publishers Series in Document Engineering 300 $aIncludes index. 311 $a87-7022-343-2 320 $aIncludes bibliographical references and index. 327 $aCover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgement -- List of Figures -- List of Tables -- List of Abbreviations -- 1: Linguistics and NLP -- 1.1 Introduction -- 1.2 General Considerations -- 1.3 Machine Learning Aspects -- 1.3.1 Machine Learning Features -- 1.3.2 Other Machine Learning Approaches -- 1.4 Design/System Considerations -- 1.4.1 Sensitivity Analysis -- 1.4.2 Iterative Tradeoff in Approach -- 1.4.3 Competition - Cooperation Algorithms -- 1.4.4 Top-Down and Bottom-Up Designs -- 1.4.5 Agent-Based Models and Other Simulations -- 1.5 Applications/Examples -- 1.6 Test and Configuration -- 1.7 Summary -- 2: Summarization -- 2.1 Introduction -- 2.2 General Considerations -- 2.2.1 Summarization Approaches - An Overview -- 2.2.2 Weighting Factors in Extractive Summarization -- 2.2.3 Other Considerations in Extractive Summarization -- 2.2.4 Meta-Algorithmics and Extractive Summarization -- 2.3 Machine Learning Aspects -- 2.4 Design/System Considerations -- 2.5 Applications/Examples -- 2.6 Test and Configuration -- 2.7 Summary -- 3: Clustering, Classification, and Categorization -- 3.1 Introduction -- 3.1.1 Clustering -- 3.1.2 Regularization - An Introduction -- 3.1.3 Regularization and Clustering -- 3.2 General Considerations -- 3.3 Machine Learning Aspects -- 3.3.1 Machine Learning and Clustering -- 3.3.2 Machine Learning and Classification -- 3.3.3 Machine Learning and Categorization -- 3.4 Design/System Considerations -- 3.5 Applications/Examples -- 3.5.1 Query-Synonym Expansion -- 3.5.2 ANOVA, Cross-Correlation, and Image Classification -- 3.6 Test and Configuration -- 3.7 Summary -- 4: Translation -- 4.1 Introduction -- 4.2 General Considerations -- 4.2.1 Review of Relevant Prior Research -- 4.2.2 Summarization as a Means to Functionally Grade the Accuracy of Translation. 327 $a4.3 Machine Learning Aspects -- 4.3.1 Summarization and Translation -- 4.3.2 Document Reading Order -- 4.3.3 Other Machine Learning Considerations -- 4.4 Design/System Considerations -- 4.5 Applications/Examples -- 4.6 Test and Configuration -- 4.7 Summary -- 5: Optimization -- 5.1 Introduction -- 5.2 General Considerations -- 5.3 Machine Learning Aspects -- 5.4 Design/System Considerations -- 5.5 Applications/Examples -- 5.5.1 Document Clustering -- 5.5.2 Document Classification -- 5.5.3 Web Mining -- 5.5.4 Information and Content Extraction -- 5.5.5 Natural Language Processing -- 5.5.6 Sentiment Analysis -- 5.5.7 Native vs. Non-Native Speakers -- 5.5.8 Virtual Reality and Augmented Reality -- 5.6 Test and Configuration -- 5.7 Summary -- 6: Learning -- 6.1 Introduction -- 6.1.1 Reading Order -- 6.1.2 Repurposing of Text -- 6.1.3 Philosophies of Learning -- 6.2 General Considerations -- 6.2.1 Metadata -- 6.2.2 Pathways of Learning -- 6.3 Machine Learning Aspects -- 6.3.1 Learning About Machine Learning -- 6.3.2 Machine Learning Constraints -- 6.4 Design/System Considerations -- 6.4.1 Do Not Use Machine Learning for the Sake of Using Machine Learning -- 6.4.2 Learning to Learn -- 6.4.3 Prediction Time -- 6.5 Applications/Examples -- 6.5.1 Curriculum Development -- 6.5.2 Customized Education Planning -- 6.5.3 Personalized Rehearsing -- 6.6 Test and Configuration -- 6.7 Summary -- 7: Testing and Configuration -- 7.1 Introduction -- 7.2 General Considerations -- 7.2.1 Data-Ops -- 7.2.2 Text Analytics and Immunological Data -- 7.2.3 Text Analytics and Cybersecurity -- 7.2.4 Data-Ops and Testing -- 7.3 Machine Learning Aspects -- 7.4 Design/System Considerations -- 7.5 Applications/Examples -- 7.6 Test and Configuration -- 7.7 Summary -- Index -- About the Author. 330 $aText analytics can provide a wide breadth ofvaluable information, including summarization, clustering, classification, andcategorization to enable better functional interaction with the text. Thisincludes improved search, translation, optimization, and learning. In this textadvanced analytical approaches used to enable improved utility of the textdocuments are described and explained. 410 0$aRiver Publishers Series in Document Engineering 606 $aText data mining 615 0$aText data mining. 676 $a006.312 702 $aSimske$b Steven 702 $aVans$b Marie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910811011803321 996 $aFunctional applications of text analytics systems$94024413 997 $aUNINA