Incorporating chunking information for Chinese named entity recognition using neural networks

Chen Lyu; Junchi Zhang; Jiangping Huang

doi:10.1117/12.2685645

15 August 2023 Incorporating chunking information for Chinese named entity recognition using neural networks

Chen Lyu, Junchi Zhang, Jiangping Huang

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127193Y (2023) https://doi.org/10.1117/12.2685645
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

Most named entity recognition (NER) systems use supervised machine learning methods, including linear models and neural networks. However, both methods require large amounts of annotated data. In this paper, we utilize chunking resources to improve the performance of NER without annotating more data and investigate two approaches to incorporate chunking information in the character-level neural network framework for Chinese NER. The first approach is multi-task learning, which falls into the framework of sharing common feature representation of Chinese chunking and NER task. The second approach is based on stacking, which uses the features provided by a trained chunking model to guide the NER model. Experimental results on OntoNotes 4.0 corpus show that compared with the models only using the NER corpus, the NER models which utilize the chunking resources can improve the performance of NER.

Citation Download Citation

Chen Lyu, Junchi Zhang, and Jiangping Huang "Incorporating chunking information for Chinese named entity recognition using neural networks", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127193Y (15 August 2023); https://doi.org/10.1117/12.2685645

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