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BL
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| Record Number
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861252
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| Main Entry
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Sidorov, Grigori
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| Title & Author
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Syntactic n-grams in computational linguistics /\ Grigori Sidorov.
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| Publication Statement
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Cham :: Springer,, 2019.
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| Series Statement
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SpringerBriefs in computer science
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| Page. NO
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1 online resource
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| ISBN
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3030147711
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: 303014772X
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: 9783030147716
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: 9783030147723
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3030147703
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9783030147709
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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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
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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
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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
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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
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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
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| Abstract
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This book is about a new approach in the field of computational linguistics related to the idea of constructing n-grams in non-linear manner, while the traditional approach consists in using the data from the surface structure of texts, i.e., the linear structure. In this book, we propose and systematize the concept of syntactic n-grams, which allows using syntactic information within the automatic text processing methods related to classification or clustering. It is a very interesting example of application of linguistic information in the automatic (computational) methods. Roughly speaking, the suggestion is to follow syntactic trees and construct n-grams based on paths in these trees. There are several types of non-linear n-grams; future work should determine, which types of n-grams are more useful in which natural language processing (NLP) tasks. This book is intended for specialists in the field of computational linguistics. However, we made an effort to explain in a clear manner how to use n-grams; we provide a large number of examples, and therefore we believe that the book is also useful for graduate students who already have some previous background in the field.
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| Subject
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Computational linguistics.
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| Subject
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Semantic computing.
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| Subject
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Computational linguistics.
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COMPUTERS-- General.
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| Subject
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Semantic computing.
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| Dewey Classification
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006.3/5
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| LC Classification
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P98
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