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<p>In Named Entity Recognition tasks, the diffusion model effectively processes discrete data. However, the original model struggles with capturing long-distance dependencies and integrating contextual information, making it difficult to recognize related entities and handle complex syntactic structures. These issues result in ambiguity and uncertainty in entity boundary recognition, affecting overall accuracy and stability. To solve this, we suggest a diffusion model with Conditional Random Fields and Bidirectional Long Short-Term Memory layers. Firstly, the BiLSTM-CRF model captures long-distance dependencies and contextual information, enhancing entity boundary recognition accuracy. Secondly, the Tversky and CRF loss functions select optimal label predictions from the probability distribution, integrating these through weighted summation to enhance sequence dependency processing and label accuracy. Thirdly, we introduce self-attention and graph attention mechanisms to handle complex data structures by processing attention probabilities, integrating with the adjacency matrix, and improving the recognition of key entity relationships. Finally, an automatic noise adjustment mechanism modifies noise levels based on performance, enhancing stability and robustness in inconsistent environments. Experiments demonstrate that this approach improves performance on several NER datasets, with significant gains in recall, accuracy, and F1 scores, making the model more robust in handling noisy and complex environments.</p>

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A diffusion enhanced CRF and BiLSTM framework for accurate entity recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月4日

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  • Yunfei Qiu,
  • Libo Dong,
  • Wenwen Zhang,
  • Haoran Xing,
  • Junwei Huang

摘要

In Named Entity Recognition tasks, the diffusion model effectively processes discrete data. However, the original model struggles with capturing long-distance dependencies and integrating contextual information, making it difficult to recognize related entities and handle complex syntactic structures. These issues result in ambiguity and uncertainty in entity boundary recognition, affecting overall accuracy and stability. To solve this, we suggest a diffusion model with Conditional Random Fields and Bidirectional Long Short-Term Memory layers. Firstly, the BiLSTM-CRF model captures long-distance dependencies and contextual information, enhancing entity boundary recognition accuracy. Secondly, the Tversky and CRF loss functions select optimal label predictions from the probability distribution, integrating these through weighted summation to enhance sequence dependency processing and label accuracy. Thirdly, we introduce self-attention and graph attention mechanisms to handle complex data structures by processing attention probabilities, integrating with the adjacency matrix, and improving the recognition of key entity relationships. Finally, an automatic noise adjustment mechanism modifies noise levels based on performance, enhancing stability and robustness in inconsistent environments. Experiments demonstrate that this approach improves performance on several NER datasets, with significant gains in recall, accuracy, and F1 scores, making the model more robust in handling noisy and complex environments.