BiGran-NER

# 🔍 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)* --- ## ✨ 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 --- ## 🧠 Model Architecture# 🔍 BiGran-NER：面向噪声临床命名实体识别的双粒度语义对齐方案 ## 📘 概览 **BiGran-NER**是一款深度学习框架，旨在提升噪声临床文本（如电子健康档案（Electronic Health Records, EHRs）、医学报告及用户生成健康内容）中的命名实体识别（Named Entity Recognition, NER）性能。该模型提出**双粒度语义对齐**策略，通过协同融合词级与句级表征，缓解Token碎片化、未登录词挑战及上下文歧义问题。 > 🔬 该方法出自论文：*"Semantic Alignment with Bi-Granularity for Enhanced Named Entity Recognition in Noisy Clinical Texts"*（BMC，2025） --- ## ✨ 核心特性 - **双粒度架构**： - 融合词级与句级嵌入表征 - 通过对比学习与动态蒸馏对齐语义与句法空间 - **双重对齐策略**： - *语义对比学习（Semantic Contrastive Learning, SCL）*：实现不同粒度下NER标记跨度的语义对齐 - *动态知识蒸馏（Dynamic Knowledge Distillation, DKD）*：将具备不确定性感知的知识从句级预测迁移至Token级预测 - **噪声鲁棒训练**： - 专为噪声化、非规范化临床语料库设计 - 可高效适配短格式或碎片化表达 --- ## 🧠 模型架构