Biomedical Relation Classification by single and multiple source domain adaptation
Sinchani Chakraborty, Sudeshna Sarkar, Pawan Goyal, Mahanandeeshwar Gattu
Abstract
Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases.- Anthology ID:
- D19-6210
- Volume:
- Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong
- Editors:
- Eben Holderness, Antonio Jimeno Yepes, Alberto Lavelli, Anne-Lyse Minard, James Pustejovsky, Fabio Rinaldi
- Venue:
- Louhi
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 75–80
- Language:
- URL:
- https://aclanthology.org/D19-6210
- DOI:
- 10.18653/v1/D19-6210
- Cite (ACL):
- Sinchani Chakraborty, Sudeshna Sarkar, Pawan Goyal, and Mahanandeeshwar Gattu. 2019. Biomedical Relation Classification by single and multiple source domain adaptation. In Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019), pages 75–80, Hong Kong. Association for Computational Linguistics.
- Cite (Informal):
- Biomedical Relation Classification by single and multiple source domain adaptation (Chakraborty et al., Louhi 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-6210.pdf