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Table of Contents
Intro; Preface; Introduction; Contents; Part I: Vector Space Model in the Analysis of Similarity between Texts; Chapter 1: Formalization in Computational Linguistics; 1.1 Computational Linguistics; 1.2 Computational Linguistics and Artificial Intelligence; 1.3 Formalization in Computational Linguistics; Chapter 2: Vector Space Model; 2.1 The Main Idea of the Vector Space Model; 2.2 Example of the Vector Space Model; 2.3 Similarity of Objects in the Vector Space Model; 2.4 Cosine Similarity Between Vectors; Chapter 3: Vector Space Model for Texts and the tf-idf Measure
3.1 Features for Text Represented in Vector Space Model3.2 Values of Text Features: tf-idf; 3.3 Term-Document Matrix; 3.4 Traditional n-grams as Features in Vector Space Model; Chapter 4: Latent Semantic Analysis (LSA): Reduction of Dimensions; 4.1 Idea of the Latent Semantic Analysis; 4.2 Examples of the Application of the Latent Semantic Analysis; 4.3 Usage of the Latent Semantic Analysis; Chapter 5: Design of Experiments in Computational Linguistics; 5.1 Machine Learning in Computational Linguistics; 5.2 Basic Concepts in the Design of Experiments; 5.3 Design of Experiments
Chapter 6: Example of Application of n-grams: Authorship Attribution Using Syllables6.1 Authorship Attribution Task; 6.2 Related Work; 6.3 Syllables and Their Use in Authorship Attribution; 6.4 Untyped and Typed Syllables; 6.5 Datasets; 6.6 Automatic Syllabification; 6.7 Experimental Methodology; 6.8 Experimental Results; Chapter 7: Deep Learning and Vector Space Model; Part II: Non-linear Construction of n-grams; Chapter 8: Syntactic n-grams: The Concept; 8.1 The Idea of Syntactic n-grams; 8.2 Previous Ideas Related to Application of Syntactic Information
8.3 Example of Continuous Syntactic n-grams in Spanish8.4 Example of Continuous Syntactic n-grams in English; Chapter 9: Types of Syntactic n-grams According to their Components; 9.1 n-grams of Lexical Elements; 9.2 n-grams of POS Tags; 9.3 n-grams of Syntactic Relations Tags; 9.4 n-grams of Characters; 9.5 Mixed n-grams; 9.6 Classification of n-grams According to their Components; Chapter 10: Continuous and Noncontinuous Syntactic n-grams; 10.1 Continuous Syntactic n-grams; 10.2 Noncontinuous Syntactic n-grams; Chapter 11: Metalanguage of Syntactic n-gram Representation
Chapter 12: Examples of Construction of Non-continuous Syntactic n-grams12.1 Example for Spanish; 12.2 Example for English; Chapter 13: Automatic Analysis of Authorship Using Syntactic n-grams; 13.1 Corpus Preparation for the Automatic Authorship Attribution Task; 13.2 Evaluation of the Authorship Attribution Task Using Syntactic n-grams; Chapter 14: Filtered n-grams; 14.1 Idea of Filtered n-grams; 14.2 Example of Filtered n-grams; 14.3 Filtered n-grams of Characters; Chapter 15: Generalized n-grams; 15.1 Idea of Generalized n-grams; 15.2 Example of Generalized n-grams; Bibliography
3.1 Features for Text Represented in Vector Space Model3.2 Values of Text Features: tf-idf; 3.3 Term-Document Matrix; 3.4 Traditional n-grams as Features in Vector Space Model; Chapter 4: Latent Semantic Analysis (LSA): Reduction of Dimensions; 4.1 Idea of the Latent Semantic Analysis; 4.2 Examples of the Application of the Latent Semantic Analysis; 4.3 Usage of the Latent Semantic Analysis; Chapter 5: Design of Experiments in Computational Linguistics; 5.1 Machine Learning in Computational Linguistics; 5.2 Basic Concepts in the Design of Experiments; 5.3 Design of Experiments
Chapter 6: Example of Application of n-grams: Authorship Attribution Using Syllables6.1 Authorship Attribution Task; 6.2 Related Work; 6.3 Syllables and Their Use in Authorship Attribution; 6.4 Untyped and Typed Syllables; 6.5 Datasets; 6.6 Automatic Syllabification; 6.7 Experimental Methodology; 6.8 Experimental Results; Chapter 7: Deep Learning and Vector Space Model; Part II: Non-linear Construction of n-grams; Chapter 8: Syntactic n-grams: The Concept; 8.1 The Idea of Syntactic n-grams; 8.2 Previous Ideas Related to Application of Syntactic Information
8.3 Example of Continuous Syntactic n-grams in Spanish8.4 Example of Continuous Syntactic n-grams in English; Chapter 9: Types of Syntactic n-grams According to their Components; 9.1 n-grams of Lexical Elements; 9.2 n-grams of POS Tags; 9.3 n-grams of Syntactic Relations Tags; 9.4 n-grams of Characters; 9.5 Mixed n-grams; 9.6 Classification of n-grams According to their Components; Chapter 10: Continuous and Noncontinuous Syntactic n-grams; 10.1 Continuous Syntactic n-grams; 10.2 Noncontinuous Syntactic n-grams; Chapter 11: Metalanguage of Syntactic n-gram Representation
Chapter 12: Examples of Construction of Non-continuous Syntactic n-grams12.1 Example for Spanish; 12.2 Example for English; Chapter 13: Automatic Analysis of Authorship Using Syntactic n-grams; 13.1 Corpus Preparation for the Automatic Authorship Attribution Task; 13.2 Evaluation of the Authorship Attribution Task Using Syntactic n-grams; Chapter 14: Filtered n-grams; 14.1 Idea of Filtered n-grams; 14.2 Example of Filtered n-grams; 14.3 Filtered n-grams of Characters; Chapter 15: Generalized n-grams; 15.1 Idea of Generalized n-grams; 15.2 Example of Generalized n-grams; Bibliography