Structure Discovery in Natural Language

  • Chris Biemann

Table of contents

  1. Front Matter
    Pages i-xx
  2. Chris Biemann
    Pages 1-17
  3. Chris Biemann
    Pages 19-37
  4. Chris Biemann
    Pages 39-71
  5. Chris Biemann
    Pages 73-100
  6. Chris Biemann
    Pages 101-111
  7. Chris Biemann
    Pages 113-144
  8. Chris Biemann
    Pages 145-155
  9. Chris Biemann
    Pages 157-160
  10. Back Matter
    Pages 161-178

About this book


Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet.

This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process?
After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction.

The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.  


68T50, 91F20, 05C82, 62H30,68T05 Applied Computer Science Computational Linguistics Natural language processing Small world graphs, complex networks

Authors and affiliations

  • Chris Biemann
    • 1
  1. 1.Department of Computer ScienceTechnical University DarmstadtDarmstadtGermany

Bibliographic information

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