Entity-Centric Information Access with Human in the Loop for the Biomedical Domain

Seid Muhie Yimam, Steffen Remus, Alexander Panchenko, Andreas Holzinger, Chris Biemann


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.
Anthology ID:
W17-8006
Volume:
Proceedings of the Biomedical NLP Workshop associated with RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Editors:
Svetla Boytcheva, Kevin Bretonnel Cohen, Guergana Savova, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
42–48
Language:
URL:
https://doi.org/10.26615/978-954-452-044-1_006
DOI:
10.26615/978-954-452-044-1_006
Bibkey:
Cite (ACL):
Seid Muhie Yimam, Steffen Remus, Alexander Panchenko, Andreas Holzinger, and Chris Biemann. 2017. Entity-Centric Information Access with Human in the Loop for the Biomedical Domain. In Proceedings of the Biomedical NLP Workshop associated with RANLP 2017, pages 42–48, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Entity-Centric Information Access with Human in the Loop for the Biomedical Domain (Yimam et al., RANLP 2017)
Copy Citation:
PDF:
https://doi.org/10.26615/978-954-452-044-1_006