  Named entity recognition (NER) plays an essential role in natural language
processing systems. Judicial NER is a fundamental component of judicial
information retrieval, entity relation extraction, and knowledge map building.
However, Chinese judicial NER remains to be more challenging due to the
characteristics of Chinese and high accuracy requirements in the judicial
filed. Thus, in this paper, we propose a deep learning-based method named
BiLSTM-CRF which consists of bi-directional long short-term memory (BiLSTM) and
conditional random fields (CRF). For further accuracy promotion, we propose to
use Adaptive moment estimation (Adam) for optimization of the model. To
validate our method, we perform experiments on judgment documents including
commutation, parole and temporary service outside prison, which is acquired
from China Judgments Online. Experimental results achieve the accuracy of
0.876, recall of 0.856 and F1 score of 0.855, which suggests the superiority of
the proposed BiLSTM-CRF with Adam optimizer.
