rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Syntactic n-grams in Computational Linguistics
  name:
    name part: Sidorov, Grigori.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: IX, 92 p. 15 illus., 10 illus. in color. online resource.
  abstract: 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.
  table of contents: Preface -- Introduction -- PART I. VECTOR SPACE MODEL IN THE ANALYSIS OF SIMILARITY BETWEEN TEXTS -- Chapter 1. Formalization in computational linguistics -- Chapter 2. Vector space model -- Chapter 3. Vector space model for texts and the tf-idf measure -- Chapter 4. Latent Semantic Analysis (LSA): reduction of dimensions -- Chapter 5. Design of experiments in computational linguistics -- Chapter 6. Example of application of n-grams: authorship attribution using n-grams of syllables -- PART II. NON-LINEAR CONSTRUCTION OF N-GRAMS -- Chapter 7. Syntactic n-grams: the concept -- Chapter 8. Types of syntactic n-grams according to their components -- Chapter 9. Continuous and non-continuous syntactic n-grams -- Chapter 10. Metalanguage of syntactic n-grams representation -- Chapter 11. Examples of construction of non-continuous syntactic n-grams -- Chapter 12. Automatic analysis of authorship using syntactic n-grams -- Chapter 13. Filtered n-grams -- Chapter 14. Generalized n-grams.
  note: by Grigori Sidorov.
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Computational linguistics
  subject:
    topic: Natural Language Processing (NLP)
  subject:
    topic: Computational Linguistics
  classification: QA76.9.N38
  classification: 006.35
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9783030147716
  identifier: https://doi.org/10.1007/978-3-030-14771-6
  record info:
    record creation date: 190402
    record change date: 20191220130324.0
    record identifier: 978-3-030-14771-6