Chinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysisof judgment documents. However, only a few researches have been devoted to this task so far. For Chinese namedentity 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 improvedthe Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a noveldataset with manual annotations is constructed. The experimental results on our corpus show that the proposedmethod is effective not only in reducing the computational time, but also in improving the effectiveness of namedentity recognition in the judicial domain.
展开▼
