@inproceedings{yimam-etal-2017-entity,
title = "Entity-Centric Information Access with Human in the Loop for the Biomedical Domain",
author = "Yimam, Seid Muhie and
Remus, Steffen and
Panchenko, Alexander and
Holzinger, Andreas and
Biemann, Chris",
editor = "Boytcheva, Svetla and
Cohen, Kevin Bretonnel and
Savova, Guergana and
Angelova, Galia",
booktitle = "Proceedings of the Biomedical {NLP} Workshop associated with {RANLP} 2017",
month = sep,
year = "2017",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd.",
url = "https://doi.org/10.26615/978-954-452-044-1_006",
doi = "10.26615/978-954-452-044-1_006",
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.",
}
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%0 Conference Proceedings
%T Entity-Centric Information Access with Human in the Loop for the Biomedical Domain
%A Yimam, Seid Muhie
%A Remus, Steffen
%A Panchenko, Alexander
%A Holzinger, Andreas
%A Biemann, Chris
%Y Boytcheva, Svetla
%Y Cohen, Kevin Bretonnel
%Y Savova, Guergana
%Y Angelova, Galia
%S Proceedings of the Biomedical NLP Workshop associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F yimam-etal-2017-entity
%X 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.
%R 10.26615/978-954-452-044-1_006
%U https://doi.org/10.26615/978-954-452-044-1_006
%P 42-48
Markdown (Informal)
[Entity-Centric Information Access with Human in the Loop for the Biomedical Domain](https://doi.org/10.26615/978-954-452-044-1_006) (Yimam et al., RANLP 2017)
ACL