基于边界识别与组合的裁判文书证据抽取方法研究

Extracting Evidences from Judgment Document via Entity Boundary Detection

裁判文书中的证据是法官量刑的基础。通过证据抽取可以对案件审判质量进行评估,从而支撑"智慧法院"建设。裁判文书中的证据大多数都比较长且存在嵌套现象,例如,"张X的身份证复印件"中的"身份证复印件",而传统的命名实体识别模型BiLSTM-CRF对较长实体和嵌套实体的识别性能较低。为了解决因裁判文书中的证据长度较长和嵌套现象而导致证据抽取性能较低的问题,该文提出了一种基于边界识别与组合的证据抽取模型。该模型首先使用BiLSTM-CRF模型识别证据的开始边界和结束边界;然后组合开始边界和结束边界,形成携带大量丰富细粒度边界信息的候选证据;最后使用基于三通道的多核CNN模型,融合细粒度的边界信息特征,对候选证据进行筛选,识别候选证据中正确的证据。实验结果表明,该文提出的模型能有效地抽取裁判文书中的证据。

Evidences in judgment document are fundamental to the human judgment of a case, and then can be applied to access the case quality or support the "Intelligent Court". To deal with the long and nested evidence extraction, this paper proposes an extraction model base on boundary detection and combination. Firstly, the BiLSTM-CRF model is used to detect begin and end boundaries of the evidences. Then assembling those boundaries into candidate evidences with plenty of fine-grained information. Finally, a tri-channel multi-core CNN classification model is applied to select the correct candidate. Experimental results show our method produces promising results.