A4 Article in conference proceedings
End-to-End System for Bacteria Habitat Extraction




List of Authors: Farrokh Mehryary, Kai Hakala, Suwisa Kaewphan, Jari Björne, Tapio Salakoski, Filip Ginter
Publication year: 2017
Book title *: SIGBioMed Workshop on Biomedical Natural Language: Proceedings of the 16th BioNLP Workshop
Number of pages: 11
ISBN: 978-1-945626-59-3

Abstract

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.


Last updated on 2019-20-07 at 13:06