A1 Refereed original research article in a scientific journal
Wide-scope biomedical named entity recognition and normalization with CRFs, fuzzy matching and character level modeling
Authors: Suwisa Kaewphan, Kai Hakala, Niko Miekka, Tapio Salakoski, Filip Ginter
Publisher: Oxford University Press
Publication year: 2018
Journal: Database: The Journal of Biological Databases and Curation
Journal acronym: Database
Volume: 2018
First page : 1
Last page: 10
eISSN: 1758-0463
DOI: https://doi.org/10.1093/database/bay096(external)
Web address : https://academic.oup.com/database/article/doi/10.1093/database/bay096/5101499(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/35859071(external)
We present a system for automatically identifying a multitude of
biomedical entities from the literature. This work is based on our
previous efforts in the BioCreative VI: Interactive Bio-ID Assignment
shared task in which our system demonstrated state-of-the-art
performance with the highest achieved results in named entity
recognition. In this paper we describe the original conditional random
field-based system used in the shared task as well as experiments
conducted since, including better hyperparameter tuning and character
level modeling, which led to further performance improvements. For
normalizing the mentions into unique identifiers we use fuzzy character n-gram
matching. The normalization approach has also been improved with a
better abbreviation resolution method and stricter guideline compliance
resulting in vastly improved results for various entity types. All tools
and models used for both named entity recognition and normalization are
publicly available under open license.
Downloadable publication This is an electronic reprint of the original article. |  |
