Named entity recognition for Chinese judgment documents based on BiLSTM and CRF
Wenming Huang, Dengrui Hu, Zhenrong Deng, Jian‐Yun Nie
Abstract
Abstract Chinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysis of judgment documents. However, only a few researches have been devoted to this task so far. For Chinese named entity recognition in judgment documents, we propose the use a bidirectional long-short-term memory (BiLSTM) model, which uses character vectors and sentence vectors trained by distributed memory model of paragraph vectors (PV-DM). The output of BiLSTM is used by conditional random field (CRF) to tag the input sequence. We also improved the Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a novel dataset with manual annotations is constructed. The experimental results on our corpus show that the proposed method is effective not only in reducing the computational time, but also in improving the effectiveness of named entity recognition in the judicial domain.