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Eliminating Incorrect Events from Large‐Scale Event Networks by Trigger Word Clustering and Pruning

Farrokh Mehryary, Suwisa Kaewphan, Kai Hakala, Filip Ginter, Eliminating Incorrect Events from Large‐Scale Event Networks by Trigger Word Clustering and Pruning. In: Olivier Bodenreider, Fabio Rinaldi, José Luis Oliveira (Eds.), Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014), 75–79, University of Aveiro, 2014.

http://dx.doi.org/10.5167/uzh-98982

Abstract:

In this short paper, we investigate hierarchical
clustering of event triggers in the EVEX largescale
event resource. As the primary application,
we utilize the clustering to identify incorrect
trigger event words and subsequently
eliminate events extracted with these triggers.
We evaluate the method on the BioNLP 2011
and 2013 Shared Task test sets and show that
the method can further increase the precision
and F-score of the winning system of the 2013
BioNLP Shared Task on Event extraction.

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Full publication in PDF-format

BibTeX entry:

@INPROCEEDINGS{inpMeKaHaGi14a,
  title = {Eliminating Incorrect Events from Large‐Scale Event Networks by Trigger Word Clustering and Pruning},
  booktitle = {Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014)},
  author = {Mehryary, Farrokh and Kaewphan, Suwisa and Hakala, Kai and Ginter, Filip},
  editor = {Bodenreider, Olivier and Rinaldi, Fabio and Oliveira, José Luis},
  publisher = {University of Aveiro},
  pages = {75–79},
  year = {2014},
}

Belongs to TUCS Research Unit(s): Turku BioNLP Group

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