基于BERT的盗窃罪法律文书命名实体识别方法

法律文书命名实体识别是智慧司法领域的关键性和基础性任务。在目前法律文书命名实体识别方法中,存在实体定义与司法业务结合不紧密、传统词向量无法解决一词多义等问题。针对以上问题,该文提出一种新的法律文本命名实体定义方案,构建了基于起诉意见书的法律文本命名实体语料集LegalCorpus;提出一种基于BERT-ON-LSTM-CRF(Bidirectional Encoder Representations from Transformers-Ordered Neuron-Long Short Term Memory Networks-Conditional Random Field)的法律文书命名实体识别方法,该方法首先利用预训练语言模型BERT根据字的上下文动态生成语义向量作为模型输入,然后运用ON-LSTM对输入进行序列和层级建模以提取文本特征,最后利用CRF获取最优标记序列。在LegalCorpus上进行实验,该文提出的方法F₁值达到86.09%,相比基线模型lattice LSTM F₁值提升了7.8%。实验结果表明,该方法可以有效对法律文书的命名实体进行识别。

BERT Based Named Entity Recognition for Legal Texts on Theft Cases

Legal named entity recognition(LNER)is a fundamentaltask for the field of smart judiciary.This paper presents a new definition of LNER and a corpus of letters of proposal for prosecution named LegalCorpus. This paper proposes novel BERT based NER model for legal texts, named BERT-ON-LSTM-CRF (Bidirectional Encoder Representations from Transformers-Ordered Neuron-Long Short Term Memory Networks-Conditional Random Field). The proposed model utilizes BERT model to dynamically obtain the semantic vectors according to the context of words. Then the ONLSTM is adopted to extract the text features by modeling the input sequence and hierarchy. Finally, the text features are decoded by CRF to obtain the optimal tag sequence. Experiments show that the proposed model can achieve a F₁-value of 86.09%, with 7.8% increased than the best baseline Lattice-LSTM.