© 2014

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


​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.


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

About the editors

After completing his doctoral dissertation with Gerhard Heyer at the University of Leipzig (Germany), Chris Biemann joined the semantic search startup Powerset (San Francisco) in 2008, which was acquired to become part of Microsoft's Bing in the same year. In 2011, he joined TU Darmstadt (Germany) as an assistant professor (W1) for Language Technology. His interests are situated in statistical semantics, unsupervised and knowledge-free natural language processing and in leveraging the wisdom of the crowds for language data acquisition. Alexander Mehler is professor (W3) for Computational Humanities / Text Technology at the Goethe University Frankfurt am Main, where he heads the Text Technology Lab as part of the Institute of Informatics. His research interests focus on the empirical analysis and simulative synthesis of discourse units in spoken and written communication. He aims at a quantitative theory of networking in linguistic systems to enable multi-agent simulations of their life cycle. Alexander Mehler integrates models of semantic spaces with simulation models of language evolution and topological models of network theory to capture the complexity of linguistic information systems. Currently, he is heading several research projects on the analysis of linguistic networks in historical semantics. Most recently he started a research project on kinetic text-technologies that integrates the paradigm of games with a purpose with the wiki way of collaborative writing and kinetic HCI.

Bibliographic information