Effective Multi-Task Learning for Biomedical Named Entity Recognition
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918103657480192 |
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| author | Ruano, João Correia, Gonçalo M. Barreiros, Leonor Mendes, Afonso |
| author_facet | Ruano, João Correia, Gonçalo M. Barreiros, Leonor Mendes, Afonso |
| contents | Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model's predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_18542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effective Multi-Task Learning for Biomedical Named Entity Recognition Ruano, João Correia, Gonçalo M. Barreiros, Leonor Mendes, Afonso Computation and Language Biomedical Named Entity Recognition presents significant challenges due to the complexity of biomedical terminology and inconsistencies in annotation across datasets. This paper introduces SRU-NER (Slot-based Recurrent Unit NER), a novel approach designed to handle nested named entities while integrating multiple datasets through an effective multi-task learning strategy. SRU-NER mitigates annotation gaps by dynamically adjusting loss computation to avoid penalizing predictions of entity types absent in a given dataset. Through extensive experiments, including a cross-corpus evaluation and human assessment of the model's predictions, SRU-NER achieves competitive performance in biomedical and general-domain NER tasks, while improving cross-domain generalization. |
| title | Effective Multi-Task Learning for Biomedical Named Entity Recognition |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.18542 |