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.