Improving Text Mining with Controlled Natural Language: A Case Study for Protein Interactions

  • Tobias Kuhn
  • Loïc Royer
  • Norbert E. Fuchs
  • Michael Schröder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4075)


Linking the biomedical literature to other data resources is notoriously difficult and requires text mining. Text mining aims to automatically extract facts from literature. Since authors write in natural language, text mining is a great natural language processing challenge, which is far from being solved. We propose an alternative: If authors and editors summarize the main facts in a controlled natural language, text mining will become easier and more powerful. To demonstrate this approach, we use the language Attempto Controlled English (ACE). We define a simple model to capture the main aspects of protein interactions. To evaluate our approach, we collected a dataset of 459 paragraph headings about protein interaction from literature. 56% of these headings can be represented exactly in ACE and another 23% partially. These results indicate that our approach is feasible.


Gene Ontology Protein Interaction Natural Language Formal Language Text Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tobias Kuhn
    • 1
    • 2
  • Loïc Royer
    • 1
  • Norbert E. Fuchs
    • 2
  • Michael Schröder
    • 1
  1. 1.TU DresdenBiotechnological CenterGermany
  2. 2.Department of InformaticsUniversity of ZurichSwitzerland

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