We introduce an end-to-end system capable
of named-entity detection, normalization
and relation extraction for extracting
information about bacteria and their habitats
from biomedical literature. Our system
is based on deep learning, CRF classifiers
and vector space models. We train
and evaluate the system on the BioNLP
2016 Shared Task Bacteria Biotope data.
The official evaluation shows that the joint
performance of our entity detection and relation
extraction models outperforms the
winning team of the Shared Task by 19pp
on F-score, establishing a new top score
for the task. We also achieve state-of-the-art
results in the normalization task.
Our system is open source and freely
available at https://github.com/
TurkuNLP/BHE.