@InProceedings{lever-jones:2017:BioNLP17,
  author    = {Lever, Jake  and  Jones, Steven},
  title     = {Painless Relation Extraction with Kindred},
  booktitle = {BioNLP 2017},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada,},
  publisher = {Association for Computational Linguistics},
  pages     = {176--183},
  abstract  = {Relation extraction methods are essential for creating robust text mining tools
	to help researchers find useful knowledge in the vast published literature.
	Easy-to-use and generalizable methods are needed to encourage an ecosystem in
	which researchers can easily use shared resources and build upon each others'
	methods. We present the Kindred Python package for relation extraction. It
	builds upon methods from the most successful tools in the recent BioNLP Shared
	Task to predict high-quality predictions with low computational cost. It also
	integrates with PubAnnotation, PubTator, and BioNLP Shared Task data in order
	to allow easy development and application of relation extraction models.},
  url       = {http://www.aclweb.org/anthology/W17-2322}
}