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Modeling, Learning, and Processing of Text-Technological Data Structures

  • Book
  • © 2012

Overview

Editors:
  1. Alexander Mehler

    1. Faculty of Linguistics and Literature, Bielefeld University, Bielefeld, Germany

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  2. Kai-Uwe Kühnberger

    1. Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany

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  3. Henning Lobin

    1. Angewandte Sprachwissenschaft und, Justus-Liebig-Universität Gießen, Gießen, Germany

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  4. Harald Lüngen

    1. Angewandte Sprachwissenschaft und, Justus-Liebig-Universität Gießen, Gießen, Germany

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  5. Angelika Storrer

    1. Institut für deutsche Sprache und Literatur, Technical University Dortmund, Dortmund, Germany

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  6. Andreas Witt

    1. SFB 441 Linguistic Data Structures, Eberhard Karls Universität Tübingen, Tübingen, Germany

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  • Focuses on procedural aspects of automatic text analysis
  • Integrates research in the upcoming and challenging text related disciplines. Such as computational linguistics, natural language processing, information retrieval, text and web mining as well as text and language technology
  • Integrates a broad range of methods from text-technology, computational linguistics and machine learning
  • Special emphasis is put on structure learning. Going beyond classical content-related text representation models in information retrieval and computational linguistics

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

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Table of contents (18 chapters)

  1. Front Matter

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

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

      • 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
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    2. The MOTS Workbench

      • Manfred Stede, Heike Bieler
      Pages 15-34

    3. Processing Text-Technological Resources in Discourse Parsing

      • 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
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    2. Semantic Distance Measures with Distributional Profiles of Coarse-Grained Concepts

      • Graeme Hirst, Saif Mohammad
      Pages 61-79

    3. Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools

      • 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
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    2. An Introduction to Hybrid Semantics: The Role of Cognition in Semantic Resources

      • Alessandro Oltramari
      Pages 97-109

    3. Modal Logic Foundations of Markup Structures in Annotation Systems

      • Marcus Kracht
      Pages 111-127

    4. Adaptation of Ontological Knowledge from Structured Textual Data

      • 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
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    2. Ten Problems in the Interpretation of XML Documents

      • C. M. Sperberg-McQueen, Claus Huitfeldt
      Pages 157-174

    3. Markup Infrastructure for the Anaphoric Bank: Supporting Web Collaboration

      • Massimo Poesio, Nils Diewald, Maik Stührenberg, Jon Chamberlain, Daniel Jettka, Daniela Goecke et al.
      Pages 175-195

    4. Integrated Linguistic Annotation Models and Their Application in the Domain of Antecedent Detection

      • Andreas Witt, Maik Stührenberg, Daniela Goecke, Dieter Metzing
      Pages 197-218

  7. Part V: Document Structure Learning

    1. Front Matter

      Pages 219-219
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    2. Machine Learning for Document Structure Recognition

      • Gerhard Paaß, Iuliu Konya
      Pages 221-247

    3. Corpus-Based Structure Mapping of XML Document Corpora: A Reinforcement Learning Based Model

      • Francis Maes, Ludovic Denoyer, Patrick Gallinari
      Pages 249-266

    4. Learning Methods for Graph Models of Document Structure

      • Peter Geibel, Alexander Mehler, Kai-Uwe Kühnberger
      Pages 267-298
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Keywords

About this book

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.

Editors and Affiliations

  • Faculty of Linguistics and Literature, Bielefeld University, Bielefeld, Germany

    Alexander Mehler

  • Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany

    Kai-Uwe Kühnberger

  • Angewandte Sprachwissenschaft und, Justus-Liebig-Universität Gießen, Gießen, Germany

    Henning Lobin, Harald Lüngen

  • Institut für deutsche Sprache und Literatur, Technical University Dortmund, Dortmund, Germany

    Angelika Storrer

  • SFB 441 Linguistic Data Structures, Eberhard Karls Universität Tübingen, Tübingen, Germany

    Andreas Witt

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