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        <title>MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition</title>
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        <namePart type="given">Li</namePart>
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            <title>Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)</title>
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            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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            <namePart type="given">Jiye</namePart>
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    <abstract>“In Chinese Named Entity Recognition, character substitution is a complicated linguistic phe-nomenon. Some Chinese characters are quite similar as they share the same components or havesimilar pronunciations. People replace characters in a named entity with similar characters togenerate a new collocation but refer to the same object. As a result, it always leads to unrecog-nizable or mislabeling errors in the NER task. In this paper, we propose a lightweight method,MFE-NER, which fuses glyph and phonetic features to help pre-trained language models handlethe character substitution problem in the NER task with limited extra cost. Basically, in the glyphdomain, we disassemble Chinese characters into Five-Stroke components to represent structurefeatures. In the phonetic domain, an improved phonetic system is proposed in our work, makingit reasonable to describe phonetic similarity among Chinese characters. Experiments demon-strate that our method performs especially well in detecting character substitutions while slightlyimproving the overall performance of Chinese NER.”</abstract>
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%0 Conference Proceedings
%T MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition
%A Jiatong, Li
%A Kui, Meng
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F jiatong-kui-2024-mfe
%X “In Chinese Named Entity Recognition, character substitution is a complicated linguistic phe-nomenon. Some Chinese characters are quite similar as they share the same components or havesimilar pronunciations. People replace characters in a named entity with similar characters togenerate a new collocation but refer to the same object. As a result, it always leads to unrecog-nizable or mislabeling errors in the NER task. In this paper, we propose a lightweight method,MFE-NER, which fuses glyph and phonetic features to help pre-trained language models handlethe character substitution problem in the NER task with limited extra cost. Basically, in the glyphdomain, we disassemble Chinese characters into Five-Stroke components to represent structurefeatures. In the phonetic domain, an improved phonetic system is proposed in our work, makingit reasonable to describe phonetic similarity among Chinese characters. Experiments demon-strate that our method performs especially well in detecting character substitutions while slightlyimproving the overall performance of Chinese NER.”
%U https://aclanthology.org/2024.ccl-1.86/
%P 1112-1122

Markdown (Informal)

[MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition](https://aclanthology.org/2024.ccl-1.86/) (Jiatong & Kui, CCL 2024)

• MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition (Jiatong & Kui, CCL 2024)

ACL

• Li Jiatong and Meng Kui. 2024. MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition. In Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference), pages 1112–1122, Taiyuan, China. Chinese Information Processing Society of China.