# A Chinese Named Entity Recognition Method Based on ERNIE-BiLSTM-CRF for Food Safety Domain
###### Abstract
To help the general public obtain food safety information from a large amount of cluttered and redundant data and to take suitable precautions in advance. Named Entity Recognition (NER) technology can quickly and accurately acquire implicit information related to food safety in internet users' comments. It is used as data support for comprehensive research and judgment, intelligent decision-making, and dynamic early warning of food safety risk situations. NER aims to identify predefined semantic types (e.g., names of people, places, organizations, and domain proper names) from text [5]. NER is one of the most fundamental and essential aspects of natural language processing (NLP), which has a wide range of applications in many scenarios, such as information extraction, question and answer systems, and machine translation. Named entities (NEs) are classified into generic classes (e.g., names of people, places) and domain-specific classes (e.g., proteins, drugs, diseases). The NER task is usually regarded as a sequence annotation task and is solved by statistical or neural network approaches [6]. With the rapid development of neural networks, deep learning-based methods [7] are widely used in NER tasks [8; 9; 10; 11] and achieve state-of-the-art results. Deep learning-based methods do not rely on the manual construction of features and can automatically obtain models by training large amounts of data. Among them, based on the excellent sequence-modeling ability of one-way Long-Short Term Memory (LSTM) models, many methods use LSTM-conditional random field (CRF) as the main framework of NER tasks and the fusion of various relevant features on this basis. BiLSTM-CRF [8] is the most common method. In [12; 13], state-of-the-art performance was achieved with this method as the main framework. In the low-resource domain, to improve entity recognition for the NER task, adversarial training is added to make the model more robust and improve the generalization ability during the training process [14; 15]. Compared with English, there are no natural boundaries in Chinese. The blurred boundaries of unitary vocabulary, complex entity structures, and diverse expressions make Chinese NER more difficult. In the field of Chinese NER, a distinction between words and characters exists in Chinese. A character-based tagging strategy is generally used to tag named entities [16; 17]. Compared with word-based and character-word union-based methods, the character-based method can avoid the problem of word separation error transmission and generally have superior performance [18; 19]. However, the character-based Chinese NER methods have a general problem: they cannot characterize the polysemy of words. It means that the same word expresses different meanings in different scenarios, but the representation of the word vector is the same, which is not consistent with the objective fact. In recent years, pre-trained language models have become a critical fundamental technology in NLP, especially the Bidirectional Encoder Representations from Transformers (BERT) [20] model proposed by Google. BERT utilizes the multilayer self-attention bidirectional modeling capability of Transformer [21] to achieve state-of-the-art (SOTA) results in several NLP tasks by predicting masked characters. Pre-trained Language Models can be used for character representation. This is because better character representations not only contain rich syntactic and semantic information but also allow the modeling of polysemous words. However, the modeling objects of BERT are mainly focused on original language signals and less on the use of semantic knowledge units for modeling. This problem is particularly evident in Chinese. For example, BERT is modeled by predicting Chinese characters, and it is difficult for the model to learn the intact semantic representation from the larger semantic units.