基于GRU的电力调度领域命名实体识别方法

电力调度领域命名实体识别是电力知识图谱构建步骤中的重要一环, 目前存在基于机器学习和深度学习模型被用于通用领域或是其他专业领域的命名实体识别. 为了解决电力调度领域命名实体识别的问题, 研究Transformer-BiGRU-CRF模型, 该模型可以有效的解决电力调度领域中命名实体识别的问题. 通过Transformer模型得到语料的字向量，再通过BiGRU和CRF进行命名实体识别。该模型在训练过程中有两种训练方式, 第1种方式是只训练BiGRU和CRF部分的参数；第2种方式是训练包括Transformer部分的整个模型的参数. 最后发现, 第1种方式达到模型的平稳状态需要的时间更少, 但是第2种达到平稳状态准确率会高出接近5%.

Named Entity Recognition in Electric Power Dispatching Field Based on GRU

Name entity recognition is an important part in the power knowledge map construction in power dispatching field. Currently, machine learning and deep learning models are used to name entity recognition in the general field or other professional fields. In order to solve the named entity recognition in the power dispatching field, the Transformer-BiGRU-CRF model is researched. The character vector of the corpus is obtained through the Transformer model, and the named entity recognition is performed through BiGRU-CRF. There are two training methods in the training process, the first method is only to train the parameters of the BiGRU-CRF part; the second method is to train the whole model parameters including the Transformer part. Finally, it is found that the first approach reaches the stationary state in less time, but the accuracy rate is about 5% higher for the second approach.