AHVE-CNER: Aligned Hanzi Visual Encoding Enhance Chinese Named Entity Recognition with Multi-Information
Xuhui Zheng, Zhiyuan Min, Bin Shi, Hao Wang
Abstract
The integration of multi-modal information, especially the graphic features of Hanzi, is crucial for improving the performance of Chinese Named Entity Recognition (NER) tasks. However, existing glyph-based models frequently neglect the relationship between pictorial elements and radicals. This paper presents AHVE-CNER, a model that integrates multi-source visual and phonetic information of Hanzi, while explicitly aligning pictographic features with their corresponding radicals. We propose the Gated Pangu-𝜋 Cross Transformer to effectively facilitate the integration of these multi-modal representations. By leveraging a multi-source glyph alignment strategy, AHVE-CNER demonstrates an improved capability to capture the visual and semantic nuances of Hanzi for NER tasks. Extensive experiments on benchmark datasets validate that AHVE-CNER achieves superior performance compared to existing multi-modal Chinese NER methods. Additional ablation studies further confirm the effectiveness of our visual alignment module and the fusion approach.
BibTeX
@inproceedings{zheng-etal-2025-ahve,
title = "{AHVE}-{CNER}: Aligned Hanzi Visual Encoding Enhance {C}hinese Named Entity Recognition with Multi-Information",
author = "Zheng, Xuhui and
Min, Zhiyuan and
Shi, Bin and
Wang, Hao",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.228/",
pages = "3391--3400"
}