IMCS-V2-NER experimental results.

<div><p>Chinese Medical Named Entity Recognition (CMNER) seeks to identify and extract medical entities from unstructured medical texts. Existing methods often depend on single-modality representations and fail to fully exploit the complementary nature of different features. This paper p...

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Bibliographic Details
Main Author:	Zhen Luo (281825) (author)
Other Authors:	Jingping Che (21526550) (author), Fan Ji (8405688) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unstructured medical texts stream network architecture recognize medical entities model &# 8217 modal global dependencies facilitate information exchange extract medical entities ensuring global consistency demonstrating significant improvements conditional random field stream attention mechanism hybrid attention mechanism multimodal information fusion extract multimodal features 26 %, 80 enhance feature representation head attention different features deep fusion 31 %, word levels sts datasets paper presents modality representations fully exploit entity predictions different modalities complementary nature based approach
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IMCS-V2-NER experimental results.

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