Modeling, Learning, and Processing of Text Technological Data Structures

  • Alexander Mehler
  • Kai-Uwe Kühnberger
  • Henning Lobin
  • Harald Lüngen
  • Angelika Storrer
  • Andreas Witt

Part of the Studies in Computational Intelligence book series (SCI, volume 370)

Table of contents

  1. Front Matter
  2. Introduction: Modeling, Learning and Processing of Text-Technological Data Structures

    1. Alexander Mehler, Kai-Uwe Kühnberger, Henning Lobin, Harald Lüngen, Angelika Storrer, Andreas Witt
      Pages 1-11
  3. Part I: Text Parsing: Data Structures, Architecture and Evaluation

    1. Front Matter
      Pages 13-13
    2. Manfred Stede, Heike Bieler
      Pages 15-34
    3. Henning Lobin, Harald Lüngen, Mirco Hilbert, Maja Bärenfänger
      Pages 35-58
  4. Part II: Measuring Semantic Distance: Methods, Resources, and Applications

    1. Front Matter
      Pages 59-59
    2. Sonya Nikolova, Jordan Boyd-Graber, Christiane Fellbaum
      Pages 81-93
  5. Part III: From Textual Data to Ontologies, from Ontologies to Textual Data

    1. Front Matter
      Pages 95-95
    2. Tonio Wandmacher, Ekaterina Ovchinnikova, Uwe Mönnich, Jens Michaelis, Kai-Uwe Kühnberger
      Pages 129-153
  6. Part IV: Multidimensional Representations: Solutions for Complex Markup

    1. Front Matter
      Pages 155-155
    2. C. M. Sperberg-McQueen, Claus Huitfeldt
      Pages 157-174
    3. Massimo Poesio, Nils Diewald, Maik Stührenberg, Jon Chamberlain, Daniel Jettka, Daniela Goecke et al.
      Pages 175-195
    4. Andreas Witt, Maik Stührenberg, Daniela Goecke, Dieter Metzing
      Pages 197-218
  7. Part V: Document Structure Learning

    1. Front Matter
      Pages 219-219
    2. Gerhard Paaß, Iuliu Konya
      Pages 221-247
    3. Francis Maes, Ludovic Denoyer, Patrick Gallinari
      Pages 249-266
    4. Peter Geibel, Alexander Mehler, Kai-Uwe Kühnberger
      Pages 267-298

About this book

Introduction

Researchers in many disciplines have been concerned with modeling textual data in order to account for texts as the primary information unit of written communication. The book “Modelling, Learning and Processing of Text-Technological Data Structures” deals with this challenging information unit. It focuses on theoretical foundations of representing natural language texts as well as on concrete operations of automatic text processing. Following this integrated approach, the present volume includes contributions to a wide range of topics in the context of processing of textual data. This relates to the learning of ontologies from natural language texts, the annotation and automatic parsing of texts as well as the detection and tracking of topics in texts and hypertexts. In this way, the book brings together a wide range of approaches to procedural aspects of text technology as an emerging scientific discipline.

Keywords

Computational Intelligence Document Classification Document Structure Mining Ontology Mining Text Parsing Topic Detection and Topic Chaining

Editors and affiliations

  • Alexander Mehler
    • 1
  • Kai-Uwe Kühnberger
    • 2
  • Henning Lobin
    • 3
  • Harald Lüngen
    • 3
  • Angelika Storrer
    • 4
  • Andreas Witt
    • 5
  1. 1.Faculty of Linguistics and LiteratureBielefeld UniversityBielefeldGermany
  2. 2.Institute of Cognitive ScienceUniversity of OsnabrückOsnabrückGermany
  3. 3.Angewandte Sprachwissenschaft undJustus-Liebig-Universität GießenGießenGermany
  4. 4.Institut für deutsche Sprache und LiteraturTechnical University DortmundDortmundGermany
  5. 5.SFB 441 Linguistic Data StructuresEberhard Karls Universität TübingenTübingenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-22613-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2012
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-22612-0
  • Online ISBN 978-3-642-22613-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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