Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature
Integration of Deep Learning and Traditional Machine Learning for Knowledge Extraction from Biomedical Literature
{Wanli Liu Jihang Mao}

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.
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| medical-concept-normalization-on-bb-norm-1 | BLAIR GMU | accuracy: 0.211 wang: 0.615 |
| medical-concept-normalization-on-bb-norm-2 | BLAIR GMU | accuracy: 0.313 wang: 0.646 |
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