Extracting and Querying Relations in Scientific Papers

  • Ulrich Schäfer
  • Hans Uszkoreit
  • Christian Federmann
  • Torsten Marek
  • Yajing Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5243)


High-precision linguistic and semantic analysis of scientific texts is an emerging research area. We describe methods and an application for extracting interesting factual relations from scientific texts in computational linguistics and language technology. We use a hybrid NLP architecture with shallow preprocessing for increased robustness and domain-specific, ontology-based named entity recognition, followed by a deep HPSG parser running the English Resource Grammar (ERG). The extracted relations in the MRS (minimal recursion semantics) format are simplified and generalized using WordNet. The resulting ‘quriples’ are stored in a database from where they can be retrieved by relation-based search. The query interface is embedded in a web browser-based application we call the Scientist’s Workbench. It supports researchers in editing and online-searching scientific papers.


Entity Recognition Language Technology Query Interface Emerge Research Area Entity Recognizer 
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 2008

Authors and Affiliations

  • Ulrich Schäfer
    • 1
  • Hans Uszkoreit
    • 1
  • Christian Federmann
    • 1
  • Torsten Marek
    • 1
  • Yajing Zhang
    • 1
  1. 1.German Research Center for Artificial Intelligence (DFKI), Language Technology LabSaarbrückenGermany

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