Conference Paper Published

Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query

Shi, K., Chen, G., Gu, J., Qian, L.*, & Zhou, G. (2024). Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query. In H. Xu, Q. Chen, H. Lin, F. Wu, L. Liu, B. Tang, T. Hao, & Z. Huang (Eds.), Health Information Processing: 9th China Health Information Processing Conference, CHIP 2023, Proceedings, 3-21. Springer.

DOI: https://doi.org/10.1007/978-981-99-9864-7_1

Abstract

Cross-lingual Named Entity Recognition (Cross-Lingual NER) addresses the challenge of NER with limited annotated data in low-resource languages by transferring knowledge from high-resource languages. Particularly, in the clinical domain, the lack of annotated corpora for Cross-Lingual NER hinders the development of cross-lingual clinical text named entity recognition. By leveraging the English clinical text corpus I2B2 2010 and the Chinese clinical text corpus CCKS2019, we construct a cross-lingual clinical text named entity recognition corpus (CLC-NER) via label alignment. Further, we propose a machine reading comprehension framework for Cross-Lingual NER using mixed language queries to enhance model transfer capabilities. We conduct comprehensive experiments on the CLC-NER corpus, and the results demonstrate the superiority of our approach over other systems.

Keywords

Clinical Text, Cross-Lingual NER, Machine Reading Comprehension, Mixed Language Query