## 1 Introduction  
[PERSON] put forward the concept of the \"Turing test\" in his famous paper \"Computing Machinery and Intelligence\" published in 1950 to judge whether a machine interacting with human has human intelligence [1]. At present, although the system that completely passes the \"Turing test\" is still far away from us, there are still some high- performance human-computer interaction products appearing in the market, such as Siri [2], ALIME [3], NetEase Qiyu [4], etc. The KBQA system is the core component of these products. Generally, these voice assistants or intelligent customer services should first use the NER module to extract the topic entity, such as a proper noun, a person's name or the name of an organization, from the text information or voice information input by the user. Then, the retrieval will be built around this extracted entity to generate the answer to users'query. Therefore, the accuracy of the NER module largely determines the performance of the whole system [5]. Traditional NER methods rely on artificial features and are difficult for mining deep semantic information, which leads to poor NER model performance when an OOV (Out of Vocabulary) problem occurs [6]. In recent years, with the continuous development of deep learning technology, DL-based (Deep Learning-based) NER methods have gradually become mainstream. Compared to a traditional one, a DL-based NER can learn more semantic features through complex non-linear transformation. In 2008, the deep NN (Neural Network) architecture proposed by [PERSON] and [PERSON] introduced the neural network architecture into the NER task for the first time [7]. The large-scale application of this technology has greatly improved the accuracy and efficiency of NER. In 2013, [PERSON] et al. [8] proposed the famous CBOW (Continuous Bag of Words) and Skip-grams models, which led to the widespread adoption of such word-embedding methods when dealing with NLP (Natural Language Processing) tasks. [PERSON] et al. [9] proposed a Bi-LSTM-CRF model, which is robust and has less dependence on word embedding. This model produced the SOTA (State Of The Art) accuracy on several NLP tasks. In 2017, the Google team proposed the Transformer model [10], which made extensive use of self-attention mechanisms to learn text representations with spectacular results. This mechanism has also been widely used in NER tasks since then. Compared to English NER tasks, Chinese NER is more difficult, since Chinese sentences cannot be naturally segmented by space like English. For Chinese NER, a common practice is to use the CWS (Chinese Word Segment) tool for word segmentation before applying word sequence labeling. However, the CWS tool cannot guarantee that all segmentations are completely correct. These potential errors can greatly affect the performance of the NER model. Despite all this, researchers have proposed some effective methods to solve such problems. [PERSON] (2018) [11] proposed a Lattice-LSTM structure model which makes full use of character information and word information in Chinese text. This model reduces segmentation errors through lexicon matching. In 2020, [PERSON]'s team proposed a FLAT (Flat-Lattice Transformer) [12] model to improve the disadvantages introduced by the use of an RNN (Recurrent Neural Network) in a Lattice-LSTM. At the same year, [PERSON] et al. [13] proposed the SoftLexicon model based on the Lattice-LSTM. The SoftLexicon model simplifies the complicated architecture of the Lattice-LSTM, making it easier to deploy to various downstream NLP tasks without affecting the structure of original system frameworks. In this work, we focus on the implementation of the Chinese NER task in the KBQA scene. We choose the commonly used Bi-LSTM-CRF model to construct the main part of the whole NER model. At the same time, considering the negative effect brought by incorrect word segmentation, we apply the SoftLexicon model to solve it.