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: Structure Discovery in Natural Language
  name:
    name part: Biemann, Chris.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XX, 180 p. online resource.
  abstract: Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems. .
  table of contents: Foreword by Antal van den Bosch -- 1.Introduction -- 2.Graph Models -- 3.SmallWorlds of Natural Language -- 4.Graph Clustering -- 5.Unsupervised Language Separation -- 6.Unsupervised Part-of-Speech Tagging -- 7.Word Sense Induction and Disambiguation -- 8.Conclusion -- References .
  note: by Chris Biemann.
  subject:
    topic: Computer science
  subject:
    topic: Artificial intelligence
  subject:
    topic: Graph theory
  subject:
    topic: Computational linguistics
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computational Linguistics
  subject:
    topic: Graph Theory
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Theory and Applications of Natural Language Processing
  identifier: 9783642259234
  identifier: http://dx.doi.org/10.1007/978-3-642-25923-4
  record info:
    record creation date: 111207
    record change date: 20151124202044.0
    record identifier: 978-3-642-25923-4