Text analytics : an introduction to the science and applications of unstructured information analysis /: an introduction to the science and applications of unstructured information analysis. (2022)

• Record Type: Book
• Further Information: Note: John Atkinson-Abutridy.
• Authors: Atkinson-Abutridy, John
• Contents: 1 TEXT ANALYTICS. 1.1 INTRODUCTION 1.2 TEXT MINING AND TEXT ANALYTICS 1.3 TASKS AND APPLICATIONS 1.4 THE TEXT ANALYTICS PROCESS 1.5 SUMMARY 1.6 QUESTIONS 2 NATURAL-LANGUAGE PROCESSING 2.1 INTRODUCTION 2.2 THE SCOPE OF NATURAL-LANGUAGE PROCESSING 2.3 NLP LEVELS AND TASKS 2.3.1 Phonology 2.3.2 Morphology 2.3.3 Lexicon 2.3.4 Syntax 2.3.5 Semantics 2.3.6 Reasoning and Pragmatics 2.1 SUMMARY 2.2 EXERCISES 2.2.1 Morphological Analysis 2.2.2 Lexical Analysis 2.2.3 Syntactic Analysis 3 INFORMATION EXTRACTION 3.1 INTRODUCTION 3.2 RULE-BASED INFORMATION EXTRACTION 3.3 NAMED-ENTITY RECOGNITION 3.3.1 N-Gram Models 3.4 RELATION EXTRACTION 3.5 EVALUATION 3.1 SUMMARY 3.2 EXERCISE 3.2.1 Regular Expressions 3.2.2 Named-Entity Recognition 4 DOCUMENT REPRESENTATION 4.1 INTRODUCTION 4.2 DOCUMENT INDEXING 4.3 VECTOR SPACE MODELS 4.3.1 Boolean Representation Model 4.3.2 Term Frequency Model 4.3.3 Inverse Document Frequency Model 4.1 SUMMARY 4.2 EXERCISES 4.2.1 TFxIDF Representation Model 5 ASSOCIATION RULES MINING 5.1 INTRODUCTION 5.2 ASSOCIATION PATTERNS 5.3 EVALUATION 5.3.1 Support 5.3.2 Confidence 5.3.3 Lift 5.4 ASSOCIATION RULES GENERATION 5.1 SUMMARY 5.2 EXERCISES 5.2.1 Extraction of Association Rules 6 CORPUS-BASED SEMANTIC ANALYSIS 6.1 INTRODUCTION 6.2 CORPUS-BASED SEMANTIC ANALYSIS 6.3 LATENT SEMANTIC ANALYSIS 6.3.1 Creating Vectors with LSA 6.4 WORD2VEC 6.4.1 Embedding Learning 6.4.2 Prediction and Embeddings Interpretation 6.1 SUMMARY 6.2 EXERCISES 6.2.1 Latent Semantic Analysis 6.2.2 Word Embedding with Word2Vec 7 DOCUMENT CLUSTERING 7.1 INTRODUCTION 7.2 DOCUMENT CLUSTERING 7.3 K-MEANS CLUSTERING 7.4 SELF-ORGANIZING MAPS 7.4.1 Topological Maps Learning 7.1 SUMMARY 7.2 EXERCISES 7.2.1 K-means Clustering 7.2.2 Self-Organizing Maps 8 TOPIC MODELING 8.1 INTRODUCTION 8.2 TOPIC MODELING 8.3 LATENT DIRICHLET ALLOCATION 8.4 EVALUATION 8.1 SUMMARY 8.2 EXERCISES 8.2.1 Modeling Topics with LDA 9 DOCUMENT CATEGORIZATION 9.1 INTRODUCTION 9.2 CATEGORIZATION MODELS 9.3 BAYESIAN TEXT CATEGORIZATION 9.4 MAXIMUM ENTROPY CATEGORIZATION 9.5 EVALUATION 9.1 SUMMARY 9.2 EXERCISES 9.2.1 Naïve Bayes Categorization 9.2.2 MaxEnt Categorization Bibliography
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2022
• Extent: 1 online resource, illustrations (black and white, and colour)
• Subjects: 006.312; Text data mining; Natural language processing (Computer science); Semantics -- Data processing; Document clustering
• Languages: English
• ISBNs: 9781000581072; 9781000581041; 9781003280996
• Related ISBNs: 9781032249797; 9781032245263
• Notes: Note: Description based on CIP data; resource not viewed.
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