دانلود کتاب Machine Learning Methods for Stylometry: Authorship Attribution and Author Profiling

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Preface
Book Structure
Hands-On Exercises and Examples
Acknowledgements
Contents
Acronyms
List of Symbols
Part I Fundamental Concepts and Models
1 Introduction to Stylistic Models and Applications
1.1 Overview and Definitions
1.2 Style and Its Explaining Factors
1.3 Authorship Attribution
1.4 Author Profiling
1.5 Forensic Issues
1.6 Author Clustering
1.7 Other Related Problems
2 Basic Lexical Concepts and Measurements
2.1 Stylometric Model
2.2 Our Running Example: The Federalist Papers
2.3 The Zipf\'s Law
2.4 Vocabulary Richness Measures
2.5 Overall Stylistic Measures
2.6 And the Letters?
3 Distance-Based Approaches
3.1 Burrows\' Delta
3.2 Kullback–Leibler Divergence Method
3.3 Labbé\'s Intertextual Distance
3.4 Other Distance Functions
3.5 Principal Component Analysis (PCA)
Part II Advanced Models and Evaluation
4 Evaluation Methodology and Test Corpora
4.1 Preliminary Remarks
4.2 Text Quality and Preprocessing
4.3 Performance Measures
4.4 Precision, Recall, and F1 Measurements
4.5 Confidence Interval
4.6 Statistical Assessment
4.7 Training and Test Sample
4.8 Classical Problems
4.9 CLEF PAN Test Collections
4.10 Evaluation Examples
5 Features Identification and Selection
5.1 Word-Based Stylistic Features
5.2 Other Stylistic Feature Extraction Strategies
5.3 Frequency-Based Feature Selection
5.4 Filter-Based Feature Selection
5.5 Wrapper Feature Selection
5.6 Characteristic Vocabulary
6 Machine Learning Models
6.1 k-Nearest Neighbors (k-NN)
6.2 Naïve Bayes
6.3 Support Vector Machines (SVMs)
6.4 Logistic Regression
6.5 Examples with R
6.5.1 K-Nearest Neighbors (k-NN)
6.5.2 Naïve Bayes
6.5.3 Support Vector Machines (SVMs)
6.5.4 Logistic Regression
7 Advanced Models for Stylometric Applications
7.1 Zeta Method
7.2 Compression Methods
7.3 Latent Dirichlet Allocation (LDA)
7.4 Verification Problem
7.5 Collaborative Authorship
7.6 Neural Network and Authorship Attribution
7.7 Distributed Language Representation
7.8 Deep Learning and Long Short-Term Memory (LSTM)
7.9 Adversarial Stylometry and Obfuscation
Part III Cases Studies
8 Elena Ferrante: A Case Study in Authorship Attribution
8.1 Corpus and Objectives
8.2 Stylistic Mapping of the Contemporary Italian Literature
8.3 Delta Model
8.4 Labbé\'s Intertextual Distance
8.5 Zeta Test
8.6 Qualitative Analysis
8.7 Conclusion
9 Author Profiling of Tweets
9.1 Corpus and Research Questions
9.2 Bots versus Humans
9.3 Man vs. Woman
9.4 Conclusion
10 Applications to Political Speeches
10.1 Corpus Selection and Description
10.2 Overall Measurements
10.3 Stylistic Similarities Between Presidencies
10.4 Characteristics Words and Sentences
10.5 Rhetoric and Style Analysis by Wordlists
10.6 Conclusion
11 Conclusion
Appendix A
A.1 Additional Resources and References
A.2 The Most Frequent Word-Types in the Federalist Papers
A.3 Proposed Features for the Federalist Papers
A.4 Feature Selection
A.5 Most Frequent Terms in Italian
A.6 US Presidents
References
Index