mlsanityData to 14 Oct 2026

HREB-CRF: Hierarchical Reduced-bias EMA for Chinese Named Entity Recognition

arXiv 2503.01217 ↗First posted 3 Mar 2025cs.CL cs.LGcited by 0

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Abstract (arXiv)

Incorrect boundary division, complex semantic representation, and differences in pronunciation and meaning often lead to errors in Chinese Named Entity Recognition(CNER). To address these issues, this paper proposes HREB-CRF framework: Hierarchical Reduced-bias EMA with CRF. The proposed method amplifies word boundaries and pools long text gradients through exponentially fixed-bias weighted average of local and global hierarchical attention. Experimental results on the MSRA, Resume, and Weibo datasets show excellent in F1, outperforming the baseline model by 1.1%, 1.6%, and 9.8%. The significant improvement in F1 shows evidences of strong effectiveness and robustness of approach in CNER tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 56 references that are in the map. First posted on arXiv 3 Mar 2025.

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References · 25

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13 more
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