Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain
Satoshi Hiai, Kazutaka Shimada, Taiki Watanabe, Akiva Miura, Tomoya Iwakura
Abstract
The number of biomedical documents is increasing rapidly. Accordingly, a demand for extracting knowledge from large-scale biomedical texts is also increasing. BERT-based models are known for their high performance in various tasks. However, it is often computationally expensive. A high-end GPU environment is not available in many situations. To attain both high accuracy and fast extraction speed, we propose combinations of simpler pre-trained models. Our method outperforms the latest state-of-the-art model and BERT-based models on the GAD corpus. In addition, our method shows approximately three times faster extraction speed than the BERT-based models on the ChemProt corpus and reduces the memory size to one sixth of the BERT ones.- Anthology ID:
- 2021.ranlp-1.60
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 530–537
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.60
- DOI:
- Cite (ACL):
- Satoshi Hiai, Kazutaka Shimada, Taiki Watanabe, Akiva Miura, and Tomoya Iwakura. 2021. Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 530–537, Held Online. INCOMA Ltd..
- Cite (Informal):
- Relation Extraction Using Multiple Pre-Training Models in Biomedical Domain (Hiai et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.ranlp-1.60.pdf