@InProceedings{rajani-bornea-barker:2017:BioNLP17,
  author    = {Rajani, Nazneen Fatema  and  Bornea, Mihaela  and  Barker, Ken},
  title     = {Stacking With Auxiliary Features for Entity Linking in the Medical Domain},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {39--47},
  abstract  = {Linking spans of natural language text to concepts in a structured source is an
	important task for many problems. It allows intelligent systems to leverage
	rich knowledge available in those sources (such as concept properties and
	relations) to enhance the semantics of the mentions of these concepts in text.
	In the medical domain, it is common to link text spans to medical concepts in
	large, curated knowledge repositories such as the Unified Medical Language
	System.
	Different approaches have different strengths: some are precision-oriented,
	some recall-oriented; some better at considering context but more prone to
	hallucination. The variety of techniques suggests that ensembling could
	outperform component technologies at this task.
	In this paper, we describe our process for building a Stacking ensemble using
	additional, auxiliary features for Entity Linking in the medical domain. We
	report experiments that show that naive ensembling does not always outperform
	component Entity Linking systems, that stacking usually outperforms naive
	ensembling, and that auxiliary features added to the stacker further improve
	its performance on three distinct datasets. Our best model produces
	state-of-the-art results on several medical datasets.},
  url       = {http://www.aclweb.org/anthology/W17-2305}
}