Chinese medical named entity recognition based on feature fusion and multihead biaffine transformations

Zhixiang Wang; Nurmemet Yolwas

doi:10.1117/12.3034807

11 July 2024 Chinese medical named entity recognition based on feature fusion and multihead biaffine transformations

Zhixiang Wang, Nurmemet Yolwas

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102A (2024) https://doi.org/10.1117/12.3034807
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Medical Named Entity Recognition (NER) plays a crucial role in enhancing the efficiency of clinical work. Currently, Chinese Named Entity Recognition methods based on deep learning models have shown significant results in this area. However, due to the differences between Chinese and English, many methods that perform well on English datasets cannot be directly transferred to the Chinese context. Additionally, identifying medical entities also poses difficulties due to the comparative scarcity of specialized medical expertise. Furthermore, the prevalent models, which are typically tailored for recognizing non-nested entities, fall short of accurately identifying nested entities within Chinese medical texts. To address these issues, we propose a Chinese medical named entity recognition model based on feature fusion and multi-head biaffine transformations. By utilizing multi-head biaffine transformations to construct span matrices and applying convolutional networks on each channel to fully model adjacent span information, we solve the problem of nested entity recognition. Additionally, we significantly enhance the accuracy of medical entity recognition by introducing Chinese medical word vectors. Finally, our research involved testing on both a nested Chinese medical entity dataset (CBLUE-CMeEE) and a non-nested medical entity dataset (CCKS2019-Yidu-S4K). The experimental results show that our proposed model improves across all metrics, becoming the new state-of-the-art (SOTA) model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhixiang Wang and Nurmemet Yolwas "Chinese medical named entity recognition based on feature fusion and multihead biaffine transformations", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102A (11 July 2024); https://doi.org/10.1117/12.3034807

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Matrices

Data modeling

Feature fusion

Education and training

Performance modeling

Convolutional neural networks

Feature extraction

Show All Keywords

Keywords/Phrases

Search In:

Publication Years