@InProceedings{yimam-EtAl:2017:BioNLP,
  author    = {Yimam, Seid Muhie  and  Remus, Steffen  and  Panchenko, Alexander  and  Holzinger, Andreas  and  Biemann, Chris},
  title     = {Entity-Centric Information Access with Human in the Loop for the Biomedical Domain},
  booktitle = {Proceedings of the Biomedical NLP Workshop associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {42--48},
  abstract  = {In this paper, we describe the concept of entity-centric information access for
	the biomedical domain. With entity recognition technologies approaching
	acceptable levels of accuracy, we put forward a paradigm of document browsing
	and searching where the entities of the domain and their relations are
	explicitly modeled to provide users the possibility of collecting exhaustive
	information on relations of interest. We describe three working prototypes
	along these lines: NEW/S/LEAK, which was developed for investigative
	journalists who need a quick overview of large leaked document collections;
	STORYFINDER, which is a personalized organizer for information found in web
	pages that allows adding entities as well as relations, and is capable of
	personalized information management; and adaptive annotation capabilities of
	WEBANNO, which is a general-purpose linguistic annotation tool. We will discuss
	future steps towards the adaptation of these tools to biomedical data, which is
	subject to a recently started project on biomedical knowledge acquisition. A
	key difference to other approaches is the centering around the user in a
	Human-in-the-Loop machine learning approach, where users define and extend
	categories and enable the system to improve via feedback and interaction.},
  url       = {https://doi.org/10.26615/978-954-452-044-1_006}
}