基于联合神经网络学习的中文电力计量命名实体识别

随着电力计量业务的不断扩展，迫切需要由业务信息、技术知识、行业标准及其内在联系所组成的电力计量知识图谱，为电网的决策和发展提供更为全面有效的支持。命名实体识别是构建知识图谱的基础。针对电力计量领域需要，结合中文分词技术特点，基于联合学习思想，提出了一种基于联合学习的中文电力计量命名实体识别技术。该技术联合CNN-BLSTM-CRF模型与整合词典知识的分词模型，使其共享实体类别和置信度；同时将2个模型的先后计算顺序改为并行计算，减少了识别误差累积。结果表明，在不需要人工构建特征的情况下，方法的正确率、召回率、F值等均显著优于以往方法。

Chinese named entity recognition in electric power metering domain based on neural joint learning

While the business of electric power metering is expanding,it is urgent to build an electric power metering knowledge graph composed of business information,technical knowledge,industry standards and their internal connections to provide more comprehensive and effective support for the decision-making and development of power grid.Named entity recognition (NER) is a fundamental task for building knowledge graph.This paper proposes an entity recognition method based on a joint learning model which considers the feature of Chinese word segmentation and ideas of multi-task learning in the electric power metering domain.The neural CNN-BLSTM-CRF model and the Chinese word segmentation model with dictionary knowledge are jointly trained to build an unified named entity recognition model which shares the entity types and confidence and changes the computing order from serial to parallel to decrease the error accumulation.The experimental results show that the proposed method is obviously better than previous methods in precision,recall rate and F-score without the need of artificial feature construction.