## 5 Conclusions  
In this work, we focused on the research of the Chinese NER task in the KBQA system. We analyzed the characteristics of input text of the KBQA system, and proposedthe an NER system suitable for KBQA scene. For the NER model, considering the problem of word segmentation in a Chinese NER task, we introduce a SoftLexicon method based on the original Bi-LSTM-CRF model. This method combines the semantic information of characters and words in the textual adopts a lexicon matching method to avoid the negative impact of word segmentation errors on the model. Different from a general NER task, for the KBQA system, we hoped to find a similar entity as the topic entity of the query when the NER model cannot recognize the correct entity to carry out subsequent tasks such as entity disambiguation and relationship recognition. Therefore, we proposed a fuzzy matching module. This module combines a traditional text matching method with a DL-based text matching method to perform fuzzy matching, which improves the robustness of the module. In addition, we used the characteristics of the data in the KBQA system to construct artificial rules to improve the efficiency of fuzzy matching. After processing by this module, for both the NLPCC2016 dataset and the power grid dataset, the accuracy of the NER reached more than 99%. In addition, using the grid-related data collected by the Hebei Electric Power Company, we carried out effective research on the construction of power grid intelligent customer service. We preprocessed the grid-related original data and tagged the data for the training of a deep learning model. Then, we extracted a lexicon of high-frequency words in the power grid domain from the dataset and used this lexicon to expand the matching lexicon used by the SoftLexicon method. The experimental results show that the improved SoftLexicon-Bi-LSTM-CRF model has significantly better performance when dealing with NER tasks in the power grid domain. At present, there are few effective research studies on a power grid intelligent customer service system in China. The NER system and the grid dataset we produced will fill the gap in the research of intelligent customer service system technology in the field of power grids. Our proposed system still has some shortcomings and limitations. For example, the system needs to be implemented with a high-frequency word matching lexicon, so it has a relatively high requirement on the quality of data in the application field. In addition, the huge amount of parameters of the BERT model may affect the efficiency of the system when deployed to the power grid intelligent customer service system. In the future, in view \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Incorrect** \\ **Results** \\ \end{tabular} & **Successfully Modified 1** & **Response Time 2** & **ACC 3** \\ \hline \hline NLPCC2016 & **225** & **135** & **0.78s** & **99.0\%** \\ \hline Power Grid & **77** & **68** & **0.23s** & **99.1\%** \\ \hline \hline \end{tabular}  
* \({}^{\dagger}\)_Successfully Modified” means that the correct entity of the input query is included in the candidate entity set generated by the fuzzy matching module. 2 \"Response Time\" means the average response time for one query. 3 \"Precision\" means the accuracy of the NER system (composed of the NER model and the fuzzy matching module)._  
\end{table} 
Table 8: Experimental results of the fuzzy matching module in the NLPCC2016 dataset and the power grid dataset. of the above problems, we will continue to improve the system. Firstly, we will try to use the transfer learning method to reduce the dependence of the model on domain data [52]. Secondly, some research has proposed some simplified BERT models, such as TinyBERT [53], MobileBERT [54] and so on, but they are difficult to use to replace the classical BERT model due to their insufficient generalization ability. In the future, we will explore the applicability of these simplified BERT models in our system.