A parser-based approach to detecting modification of biomedical events

  • Authors:
  • Andrew MacKinlay;David Martinez;Timothy Baldwin

  • Affiliations:
  • NICTA & University of Melbourne, Melbourne, Australia;NICTA & University of Melbourne, Melbourne, Australia;NICTA & University of Melbourne, Melbourne, Australia

  • Venue:
  • Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics
  • Year:
  • 2011

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Abstract

This work describes a system for identifying event mentions in bio-molecular text that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). Our system combines a simple bag-of-words approach with two grammar-based approaches, namely the English Resource Grammar and the RASP parser. We interpret the output of the respective parsers via MRS semantics, and feed them into a machine learner. Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.