# A Chinese Named Entity Recognition Method Based on ERNIE-BiLSTM-CRF for Food Safety Domain
## 2 Related Work  
A deep learning-based approach is used for unstandardized Chinese NER in our work. [PERSON] et al. [8] proposed a CNN-CRF model based on a convolutional neural network (CNN), which obtained competitive performance compared to various best statistical models. [PERSON] et al. [9] proposed a BiLSTM-CRF model to solve the text sequence labeling problem as a benchmark model. [PERSON] et al. [25] and [PERSON] et al. [11] infused character features extracted by CNN to enhance the word-level representation based on the BiLSTM-CRF framework. In the low-resource domain, [PERSON] et al. [16] enhanced the robustness of NER models by adding adversarial perturbations to the original samples. In the Chinese NER task, [PERSON] et al. [26] composed a sequence of word roots for each character and used LSTM networks to obtain the root information of Chinese characters. [PERSON] et al. [27] proposed the Lattice-LSTM method by replacing the traditional LSTM cells with a lattice LSTM. It cleverly encodes the Chinese characters and all the potential words matched with the lexicon. Based on the Lattice-LSTM, [PERSON] et al. [28] proposed the word-character LSTM (WC-LSTM) model to alleviate the impact of word separation errors by adding word information to the start and end characters of a word. Chinese characters, as pictographs, contain potential glyph information. [PERSON]. [29] proposed the fused glyph network FGN to extract the interaction information between distributed representations of characters and glyph representations through a fusion mechanism. Ref. [30] proposed the FLAT model by converting the Lattice structure into a planar structure composed of spans. This model makes full use of Lattice information and has good parallelization capability based on the power of the Transformer and well-designed positional encoding. The SLK-NER model proposed in [31] uses global semantic information to fuse lexical knowledge through an attention mechanism to integrate more informative words into a character-based model and alleviate lexical boundary conflicts. In NER domain research, introducing extra knowledge is a common way to improve model performance. [PERSON]. [32] proposed an AESINER model that efficiently uses attention integration to encode and fuse different types of syntactic information (e.g., lexical annotation, constituent syntactic information, and dependent syntactic information) to help the model identify named entities. In social media such as Weibo and Twitter, many short texts generated by users contain various types of entities. Some entities are not written following standard syntactic conventions (e.g., abbreviated by users at will), resulting in a small probability of such entities showing sparsity, making recognizing such entities more difficult. In response to this question, previous studies have used domain information (e.g., gazetteers and embeddings trained on prominent social media texts) and external features (e.g., lexical tags) to help improve the performance of NER on social media [33; 34]. However, these methods require extra work to obtain this information, and there is noise in the results. For example, training embeddings in the social media domain can bring many unique expressions to the vocabulary. Therefore, [35] proposed the SA-NER model to enhance the recognition of named entities with semantic expansion. However, the F1 value of this model only reached 69.8% on the Weibo dataset. However, most of the studies in the literature above are based on canonical texts and large-scale corpora. In contrast, the food safety domain corpus is scarce, and the implicit information in non-canonical texts on the web is not fully utilized. In addition, the above papers rarely enhance the entity recognition performance based on the features of the irregular Chinese text. Therefore, to better represent syntactic semantic information in different contexts, we use ERNIE, a pre-training model more suitable for the non-standardized Chinese text NER task.