Abstract
With the development of medical information management, numerous medical data are being classified, indexed, and searched in various systems. Disease phrase matching, i.e., deciding whether two given disease phrases interpret each other, is a basic but crucial preprocessing step for the above tasks. Being capable of relieving the scarceness of annotations, domain adaptation is generally considered useful in medical systems. However, efforts on applying it to phrase matching remain limited. This paper presents a domain-adaptive matching network for disease phrases. Our network achieves domain adaptation by adversarial training, i.e., preferring features indicating whether the two phrases match, rather than which domain they come from. Experiments suggest that our model has the best performance among the very few non-adaptive or adaptive methods that can benefit from out-of-domain annotations.- Anthology ID:
- W18-2315
- Volume:
- Proceedings of the BioNLP 2018 workshop
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 137–141
- Language:
- URL:
- https://aclanthology.org/W18-2315
- DOI:
- 10.18653/v1/W18-2315
- Cite (ACL):
- Miaofeng Liu, Jialong Han, Haisong Zhang, and Yan Song. 2018. Domain Adaptation for Disease Phrase Matching with Adversarial Networks. In Proceedings of the BioNLP 2018 workshop, pages 137–141, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Domain Adaptation for Disease Phrase Matching with Adversarial Networks (Liu et al., BioNLP 2018)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W18-2315.pdf