  Background: Named Entity Recognition (NER) and Normalisation (NEN) are core
components of any text-mining system for biomedical texts. In a traditional
concept-recognition pipeline, these tasks are combined in a serial way, which
is inherently prone to error propagation from NER to NEN. We propose a parallel
architecture, where both NER and NEN are modeled as a sequence-labeling task,
operating directly on the source text. We examine different harmonisation
strategies for merging the predictions of the two classifiers into a single
output sequence. Results: We test our approach on the recent Version 4 of the
CRAFT corpus. In all 20 annotation sets of the concept-annotation task, our
system outperforms the pipeline system reported as a baseline in the CRAFT
shared task 2019. Conclusions: Our analysis shows that the strengths of the two
classifiers can be combined in a fruitful way. However, prediction
harmonisation requires individual calibration on a development set for each
annotation set. This allows achieving a good trade-off between established
knowledge (training set) and novel information (unseen concepts). Availability
and Implementation: Source code freely available for download at
https://github.com/OntoGene/craft-st. Supplementary data are available at arXiv
online.
