## 1 Introduction
However, the SoftLexicon model only shows good performance on MSRA [14], Weibo [15], OntoNotes [16] and other public domain datasets. Whether it can be applied to a domain-specific NER task needs to be verified. Therefore, in addition to utilizing the open-source Chinese public domain dataset NLPCC2016 [17], we also utilize the power-grid-related dataset collected by the Hebei Electric Power Company for experiments. The result shows that the performance of the SoftLexicon-based NER model can be improved by expanding the matching lexicon according to the high-frequency words of the application domain. In a typical KBQA system, the entities recognized by the NER model will be used to establish a connection between the knowledge base and the user input query. If the entities recognized by the NER model cannot be retrieved in the knowledge base, the knowledge-base-based queries cannot be carried out. Therefore, a complete KBQA system should have the function of fuzzy matching so that those incorrectly recognized entities can retrieve their relevant information in the knowledge base. Some researchers choose to realize this function by introducing deep learning methods [18; 19]. For example, [PERSON] et al. [20] proposed a method that uses CNN (Convolutional Neural Networks) to learn the vector representation of text and then uses cosine similarity scores between candidate entity vectors and text vectors to realize the matching function. In this work, we regard the fuzzy matching function as a continuation of NER. According to the analysis of NER model recognition results and specific application scenarios, we propose a fuzzy matching module based on the combination of multiple methods. This module combines artificial rules with deep learning methods, which can efficiently recall the entity set with high similarity to the entity to be matched from the knowledge base. The user or system can select an entity from the set to modify the recognition result of the NER model, thereby improving the performance of the named entity recognition module. The contributions of this work can be summarized as follows:  
* We verify the performance of the SoftLexicon+Bi-LSTM-CRF model in NER tasks under a KBQA scenario. Moreover, we explore the applicability of this model in the non-public field, such as a power grid field, and improve the SoftLexicon method according to the application domain. * To improve the performance of NER, we propose an efficient fuzzy matching module that can modify those entities incorrectly recognized by the NER model based on the combination of multiple methods. This module can be easily deployed in a KBQA system and has strong portability and robustness. * We further build a dataset and lexicon related to a power grid based on the data provided by the Hebei Electric Power Company and use them to construct an NER system suitable for the power grid field. The experimental results show that the accuracy of the improved SoftLexicon+Bi-LSTM-CRF model on the power grid dataset is 91.24%, the recall rate is 93.65% and the F1-score is 92.43%. After processing the recognition results by using the fuzzy matching module, about 99% of the entities in the test dataset can be correctly recognized. The experiment results prove that the system achieves good performance on the NER task of the power grid field. Moreover, this system, including the power grid dataset and the power grid lexicon produced by us, can be applied to the construction of a KBQA module of power-grid-related smart customer service system.