python毕业设计&课设-基于BERT+BiLSTM+CRF实现中文命名实体识别(源码+文档).zip

上传者: 59708493 | 上传时间: 2024-07-02 15:37:12 | 文件大小: 801KB | 文件类型: ZIP
BERT+BiLSTM+CRF是一种用于中文命名实体识别(Named Entity Recognition,简称NER)的模型,结合了BERT模型、双向长短时记忆网络(Bidirectional LSTM)和条件随机场(CRF)。 BERT是一种预训练的深度双向变换器模型,具有强大的自然语言处理能力。它能够学习上下文相关的语义表示,对于NLP任务非常有用。 BiLSTM是一种循环神经网络,能够捕捉上下文之间的依赖关系。通过同时考虑前向和后向上下文,BiLSTM能够更好地理解句子中实体的边界和内部结构。 CRF是一种概率图模型,常用于序列标注任务。它能够基于输入序列和概率分布进行标签推断,使得预测的标签序列具有全局一致性。 在BERT+BiLSTM+CRF模型中,首先使用BERT模型提取句子中的特征表示。然后,将这些特征输入到BiLSTM中,通过双向上下文的学习,得到更丰富的句子表示。最后,使用CRF层对各个词的标签进行推断,并输出最终的实体识别结果。 这种模型的优势在于能够充分利用BERT的语义信息和BiLSTM的上下文依赖性,同时通过CRF层对标签进行约束,提高了实体识别的

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