End-to-End System for Bacteria Habitat Extraction

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-ofthe-art results in the normalization task. Our system is open source and freely available at https://github.com/ TurkuNLP/BHE.