BiGran-NER
收藏Zenodo2025-07-24 更新2026-05-26 收录
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https://zenodo.org/doi/10.5281/zenodo.16410690
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# 🔍 BiGran-NER: Semantic Alignment with Bi-Granularity for Noisy Clinical NER
## 📘 Overview
**BiGran-NER** is a deep learning framework that enhances named entity recognition (NER) performance in noisy clinical texts, such as those found in electronic health records (EHRs), medical reports, and user-generated health content. The model introduces a **Bi-Granularity Semantic Alignment** strategy that synergizes word-level and sentence-level representations to mitigate token fragmentation, out-of-vocabulary challenges, and contextual ambiguity.
> 🔬 Proposed in: *"Semantic Alignment with Bi-Granularity for Enhanced Named Entity Recognition in Noisy Clinical Texts" (BMC, 2025)*
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## ✨ Key Features
- **Bi-Granularity Architecture**: - Combines token-level and sentence-level embeddings - Aligns semantic and syntactic spaces via contrastive and dynamic distillation
- **Dual Alignment Strategies**: - *Semantic Contrastive Learning (SCL)* to align NER token spans across granularity levels - *Dynamic Knowledge Distillation (DKD)* to transfer uncertainty-aware knowledge from sentence to token predictions
- **Noise-Robust Training**: - Tailored for noisy, informal clinical corpora - Efficient adaptation to short-form or fragmented expressions
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## 🧠 Model Architecture
提供机构：
Zenodo
创建时间：
2025-07-24
