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http://hdl.handle.net/10397/107981
Title: | Cross-lingual name entity recognition from clinical text using mixed language query | Authors: | Shi, K Chen, G Gu, J Qian, L Zhou, G |
Issue Date: | 2024 | Source: | Communications in computer and information science, 2024, v. 1993, p. 3-21 | 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 |
Publisher: | Springer | Journal: | Communications in computer and information science | ISSN: | 1865-0929 | EISSN: | 1865-0937 | DOI: | 10.1007/978-981-99-9864-7_1 | Description: | 9th China Health Information Processing Conference, CHIP 2023, Hangzhou, China, October 27-29, 2023, |
Appears in Collections: | Conference Paper |
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