Named Entity Recognition in Context
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913760795426816 |
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| author | Brisson, Colin Kahfy, Ayoub Bui, Marc Constant, Frédéric |
| author_facet | Brisson, Colin Kahfy, Ayoub Bui, Marc Constant, Frédéric |
| contents | We present the Named Entity Recognition system developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach integrates three core components: (1) Pindola, a modern transformer-based bidirectional encoder pretrained on a large corpus of Classical Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20836 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Named Entity Recognition in Context Brisson, Colin Kahfy, Ayoub Bui, Marc Constant, Frédéric Computation and Language We present the Named Entity Recognition system developed by the Edit Dunhuang team for the EvaHan2025 competition. Our approach integrates three core components: (1) Pindola, a modern transformer-based bidirectional encoder pretrained on a large corpus of Classical Chinese texts; (2) a retrieval module that fetches relevant external context for each target sequence; and (3) a generative reasoning step that summarizes retrieved context in Classical Chinese for more robust entity disambiguation. Using this approach, we achieve an average F1 score of 85.58, improving upon the competition baseline by nearly 5 points. |
| title | Named Entity Recognition in Context |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.20836 |