Text Mining

From Ontology Learning to Automated Text Processing Applications

  • Chris Biemann
  • Alexander Mehler

Table of contents

  1. Front Matter
    Pages i-x
  2. Text Mining Techniques and Methodologies

    1. Front Matter
      Pages 1-1
    2. Uwe Quasthoff, Dirk Goldhahn, Thomas Eckart
      Pages 3-24
    3. Zornitsa Kozareva
      Pages 41-62
    4. Patrick Oesterling, Christian Heine, Gunther H. Weber, Gerik Scheuermann
      Pages 63-85
    5. Alexander Mehler, Tim vor der Brück, Rüdiger Gleim, T. Geelhaar
      Pages 87-112
  3. Text Mining Applications

    1. Front Matter
      Pages 113-113
    2. Stefan Bordag, Christian Hänig, Christian Beutenmüller
      Pages 115-136
    3. Veronica Perez-Rosas, Cristian Bologa, Mihai Burzo, Rada Mihalcea
      Pages 157-175
    4. Jonathan Sonntag, Manfred Stede
      Pages 177-199
    5. Marco Büchler, Philip R. Burns, Martin Müller, Emily Franzini, Greta Franzini
      Pages 221-238

About this book

Introduction

​This book comprises a set of articles that specify the methodology of text mining, describe the creation of lexical resources in the framework of text mining, and use text mining for various tasks in natural language processing (NLP). The analysis of large amounts of textual data is a prerequisite to build lexical resources such as dictionaries and ontologies, and also has direct applications in automated text processing in fields such as history, healthcare and mobile applications, just to name a few. This volume gives an update in terms of the recent gains in text mining methods and reflects the most recent achievements with respect to the automatic build-up of large lexical resources. It addresses researchers that already perform text mining, and those who want to enrich their battery of methods. Selected articles can be used to support graduate-level teaching.

The book is suitable for all readers that completed undergraduate studies of computational linguistics, quantitative linguistics, computer science and computational humanities. It assumes basic knowledge of computer science and corpus processing as well as of statistics.

Keywords

Big Data Corpus processing Dictionary acquisition Natural Language Processing Text mining

Editors and affiliations

  • Chris Biemann
    • 1
  • Alexander Mehler
    • 2
  1. 1.Computer Science DepartmentTechnische Universität Darmstadt FG Language TechnologyDarmstadtGermany
  2. 2.Computer Science DepartmentGoethe University WG Text TechnologyFrankfurt am MainGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-12655-5
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-12654-8
  • Online ISBN 978-3-319-12655-5
  • Series Print ISSN 2192-032X
  • Series Online ISSN 2192-0338
  • About this book