基于多模型融合的电力运检命名实体识别

为有效解决构建电力运检知识图谱的关键步骤之一的电力运检命名实体识别问题,通过构建一种基于Stacking多模型融合的隐马尔可夫-条件随机场-双向长短期记忆网络(hidden Markov-conditional random fields-bi-directional long short-term,HCB)模型方法研究了电力运检命名实体识别问题。HCB模型分为两层,第一层使用隐马尔可夫模型(hidden Markov model,HMM)、条件随机场(conditional random fields,CRF)和双向长短期记忆网络(bi-directional long short-term memory,Bi-LSTM)模型进行训练预测,再将预测结果输入第二层的CRF模型进行训练,经过双层模型训练预测得出最后的命名实体。结果表明:在电力运检命名实体识别问题上HCB模型的精确率、召回率及F1值等指标明显优于单模型以及其他的融合模型。可见HCB模型能有效解决电力运检命名实体识别问题。

Named Entity Recognition in Power Operation Inspection Based on Multi-model Fusion

In order to effectively solve the power operation and inspection named entity identification problem, which is one of the key steps in building the knowledge graph of power operation and inspection, a Hidden Markov - Conditional Random Fields - Bi-directional Long Short-Term(HCB) model approach based on Stacking multi-model fusion is use to investigate the power operation and inspection named entity identification problem.HCB model is divided into two layers. The first layer uses Hidden Markov Model(HMM), Conditional Random Fields(CRF)and Bi-directional Long Short-Term Memory(Bi-LSTM) model for training and prediction, and then inputs the prediction results into the second layer CRF model for training, and obtains the final named entity through the training of the two-layer model. The results show that the HCB model is significantly better than other models in terms of precision, recall rate and F1 value on the identification of named entities for power operation and inspection. It is concluded that the HCB model can effectively solve the power operation and inspection named entity identification problem.