@inproceedings{d6e2fc65ed0a47b3bfcd220190af8196,
title = "Cross-Lingual Name Entity Recognition from Clinical Text Using Mixed Language Query",
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",
author = "Kunli Shi and Gongchi Chen and Jinghang Gu and Longhua Qian and Guodong Zhou",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 9th China Health Information Processing Conference, CHIP 2023 ; Conference date: 27-10-2023 Through 29-10-2023",
year = "2024",
month = feb,
day = "1",
doi = "10.1007/978-981-99-9864-7\_1",
language = "English",
isbn = "9789819998630",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "3--21",
editor = "Hua Xu and Qingcai Chen and Hongfei Lin and Fei Wu and Lei Liu and Buzhou Tang and Tianyong Hao and Zhengxing Huang",
booktitle = "Health Information Processing",
address = "Germany",
}