@InProceedings{thorne-klinger:2017:BioNLP,
  author    = {Thorne, Camilo  and  Klinger, Roman},
  title     = {Towards Confidence Estimation for Typed Protein-Protein Relation Extraction},
  booktitle = {Proceedings of the Biomedical NLP Workshop associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {55--63},
  abstract  = {Systems which build on top of information extraction are typically
	  challenged to extract knowledge that, while correct, is not yet well-known. 
	  We hypothesize that a good
	  confidence measure for relational information has the property that
	  such interesting information is found between information
	  extracted with very high confidence and very low confidence.
	  We discuss confidence estimation for the domain of biomedical
	  protein-protein relation discovery in biomedical literature. As
	  facts reported in papers take some time to be validated and recorded
	  in biomedical databases, such task gives rise to large quantities of
	  unknown but potentially true candidate relations.  It is thus
	  important to rank them based on supporting evidence rather than
	  discard them.
	  In this paper, we discuss this task and propose different approaches
	  for confidence estimation and a pipeline to evaluate such
	  methods. We show that the most straight-forward approach, a
	  combination of different confidence measures from pipeline modules
	  seems not to work well. We discuss this negative result and pinpoint
	  potential future research directions.},
  url       = {https://doi.org/10.26615/978-954-452-044-1_008}
}