Syntactic Wordclass Tagging

  • Hans van Halteren
Part of the Text, Speech and Language Technology book series (TLTB, volume 9)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. The User’s View

    1. Front Matter
      Pages 1-1
    2. Atro Voutilainen
      Pages 3-7
    3. Atro Voutilainen
      Pages 9-21
    4. Geoffrey Leech, Nicholas Smith
      Pages 23-36
    5. Jan Cloeren
      Pages 37-54
    6. Geoffrey Leech, Andrew Wilson
      Pages 55-80
    7. Hans van Halteren
      Pages 81-94
    8. Hans van Halteren
      Pages 95-104
  3. The Implementer’s View

    1. Front Matter
      Pages 107-107
    2. Hans van Halteren, Atro Voutilainen
      Pages 109-115
    3. Gregory Grefenstette
      Pages 117-133
    4. Anne Schiller, Lauri Karttunen
      Pages 135-147
    5. Monica Monachini, Nicoletta Calzolari
      Pages 149-174
    6. Kemal Oflazer
      Pages 175-205
    7. Eric Brill
      Pages 207-216
    8. Atro Voutilainen
      Pages 217-246
    9. Eric Brill
      Pages 247-262
    10. Marc El-Beze, Bernard Merialdo
      Pages 263-284
    11. Walter Daelemans
      Pages 285-304

About this book

Introduction

In both the linguistic and the language engineering community, the creation and use of annotated text collections (or annotated corpora) is currently a hot topic. Annotated texts are of interest for research as well as for the development of natural language pro­ cessing (NLP) applications. Unfortunately, the annotation of text material, especially more interesting linguistic annotation, is as yet a difficult task and can entail a substan­ tial amount of human involvement. Allover the world, work is being done to replace as much as possible of this human effort by computer processing. At the frontier of what can already be done (mostly) automatically we find syntactic wordclass tagging, the annotation of the individual words in a text with an indication of their morpho syntactic classification. This book describes the state of the art in syntactic wordclass tagging. As an attempt to give an overall view of the field, this book is of interest to (at least) two, possibly very different, types of reader. The first type consists of those people who are using, or are planning to use, tagged material and taggers. They will want to know what the possibilities and impossibilities of tagging are, but are not necessarily interested in the internal working of automatic taggers. This, on the other hand, is the main interest of our second type of reader, the builders of automatic taggers and other natural language processing software.

Keywords

Markov model hidden markov model learning machine learning performance

Editors and affiliations

  • Hans van Halteren
    • 1
  1. 1.University of NijmegenThe Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-015-9273-4
  • Copyright Information Springer Science+Business Media B.V. 1999
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-90-481-5296-4
  • Online ISBN 978-94-015-9273-4
  • Series Print ISSN 1386-291X
  • About this book