TimeML-compliant text analysis for temporal reasoning

  • Authors:
  • Branimir Boguraev;Rie Kubota Ando

  • Affiliations:
  • IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY

  • Venue:
  • IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
  • Year:
  • 2005

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Abstract

Reasoning with time needs more than just a list of temporal expressions. TimeML--an emerging standard for temporal annotation as a language capturing properties and relationships among timedenoting expressions and events in text--is a good starting point for bridging the gap between temporal analysis of documents and reasoning with the information derived from them. Hard as TimeML-compliant analysis is, the small size of the only currently available annotated corpus makes it even harder. We address this problem with a hybrid TimeML annotator, which uses cascaded finite-state grammars (for temporal expression analysis, shallow syntactic parsing, and feature generation) together with a machine learning component capable of effectively using large amounts of unannotated data.