Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature

Jihang Mao, Wanli Liu


Abstract
In this paper, we present our participation in the Bacteria Biotope (BB) task at BioNLP-OST 2019. Our system utilizes fine-tuned language representation models and machine learning approaches based on word embedding and lexical features for entities recognition, normalization and relation extraction. It achieves the state-of-the-art performance and is among the top two systems in five of all six subtasks.
Anthology ID:
D19-5724
Volume:
Proceedings of the 5th Workshop on BioNLP Open Shared Tasks
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kim Jin-Dong, Nédellec Claire, Bossy Robert, Deléger Louise
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
168–173
Language:
URL:
https://aclanthology.org/D19-5724
DOI:
10.18653/v1/D19-5724
Bibkey:
Cite (ACL):
Jihang Mao and Wanli Liu. 2019. Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature. In Proceedings of the 5th Workshop on BioNLP Open Shared Tasks, pages 168–173, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature (Mao & Liu, BioNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-5/D19-5724.pdf
Data
BBBB-norm-habitatBB-norm-phenotype