Public articles linked to the same research event.
arXiv The work proposes MITE, which reformulates biomedical named entity recognition as a structure-to-structure generation task by rendering each training instance in multiple programming-language formats such as Python, C++, and Java to supply structurally diverse supervision without external biomedical knowledge or extra annotations, and at inference aggregates predictions from the different code formats through entity-level voting; on six widely used BioNER datasets MITE consistently outperforms representative BERT-based and LLM-based baselines and shows strong cross-dataset generalization, with ablation and parameter analyses further supporting the effectiveness and robustness of the proposed components.
The work proposes MITE, which reformulates biomedical named entity recognition as a structure-to-structure generation task by rendering each training instance in multiple programming-language formats such as Python, C++, and Java to supply structurally diverse supervision without external biomedical knowledge or extra annotations, and at inference aggregates predictions from the different code formats through entity-level voting; on six widely used BioNER datasets MITE consistently outperforms representative BERT-based and LLM-based baselines and shows strong cross-dataset generalization, with ablation and parameter analyses further supporting the effectiveness and robustness of the proposed components.
The work proposes MITE, which reformulates biomedical named entity recognition as a structure-to-structure generation task by rendering each training instance in multiple programming-language formats such as Python, C++, and Java to supply structurally diverse supervision without external biomedical knowledge or extra annotations, and at inference aggregates predictions from the different code formats through entity-level voting; on six widely used BioNER datasets MITE consistently outperforms representative BERT-based and LLM-based baselines and shows strong cross-dataset generalization, with ablation and parameter analyses further supporting the effectiveness and robustness of the proposed components.
The work proposes MITE, which reformulates biomedical named entity recognition as a structure-to-structure generation task by rendering each training instance in multiple programming-language formats such as Python, C++, and Java to supply structurally diverse supervision without external biomedical knowledge or extra annotations, and at inference aggregates predictions from the different code formats through entity-level voting; on six widely used BioNER datasets MITE consistently outperforms representative BERT-based and LLM-based baselines and shows strong cross-dataset generalization, with ablation and parameter analyses further supporting the effectiveness and robustness of the proposed components.