A Knowledge-driven Generative Model for Multi-implication Chinese Medical Procedure Entity Normalization
Jinghui Yan | Yining Wang | Lu Xiang | Yu Zhou | Chengqing Zong
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures present challenges in our problem. The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions. We propose a sequence generative framework to directly generate all the corresponding medical procedure entities. we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results. The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures.
Extraction of Bilingual Technical Terms for Chinese-Japanese Patent Translation
Wei Yang | Jinghui Yan | Yves Lepage
Proceedings of the NAACL Student Research Workshop
- Wei Yang 1
- Yves Lepage 1
- Yining Wang 1
- Lu Xiang 1
- Yu Zhou 1
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