###### Abstract  
Due to the fragile physicochemical properties of hazardous chemicals, the chances of leakage and explosion during production, transportation, and storage are quite high. In recent years, hazardous chemical accidents have occurred frequently, posing a great threat to people's lives and property. Hence, it is crucial to analyze hazardous chemical accidents and establish corresponding warning mechanisms and safeguard measures. At present, most hazardous-chemical-accident data exist in text format. However, named entity recognition (NER), as a method to extract useful information from text data, has not been fully utilized in the field of Chinese hazardous-chemical handling. The challenge is that Chinese NER is more difficult than English NER, because the boundaries of Chinese are fuzzy. In addition, the descriptions of hazardous chemical accidents are colloquial and lacks relevant labeling data. Further, most current models do not consider identifying the entities related to accident scenarios, losses, and causes. To tackle these issues, we propose a model based on a rule template and Bert-BiLSTM-CRF (RT-BBC) to recognize named entities from unstructured Chinese hazardous chemical accident reports. Comprehensive experiments on real-world datasets show the effectiveness of the proposed method. Specifically, RT-BBC outperformed the most competitive method by 6.6% and 3.6% in terms of accuracy and F1. named entity recognition; rule templates; pre-trained model; hazardous chemicals +
Footnote †: journal:  
It is challenging to identify entity boundaries in Chinese writing, since there are no spaces;  
Word segmentation is often required before Chinese named entity recognition, and various word segmentation algorithms yield substantially different results;  
Even the same entity in Chinese can have completely different meanings. In fact, hazardous chemical accidents are mainly reported in the form of radio news reports or text, with obvious colloquialisms, which makes entity recognition more challenging. At present, a series of methods have been proposed for Chinese named entity recognition, which focus on the extraction of domain features [9; 10; 11]. However, due to the lack of professional knowledge, current methods cannot accurately identify hazardous chemical entities. In addition, the existing methods also ignore the identification of accident scenarios, losses, and causal entities. To solve the above problems, we propose a Chinese hazardous-chemical-accident information entity recognition approach based on a rule template and pre-trained model. Rule templates use pattern matching and string matching to recognize the entities with  
Figure 1: An example of accident information entity recognition. structural features that are few in number (such as date and time). The entities with poor structural features but present in large numbers (such as personnel and causes) are instead identified using the Bi-LSTM-CRF model. In order to increase the recognition accuracy, we also process the source data directly using pre-trained word vector technology. The main contributions are as follows:  
1. We design a collection tool to crawl the information of hazardous chemical accidents, and through pre-processing processes, we offer data support for accident analysis. 2. We built rule templates and employ dictionaries, libraries, and other techniques to recognize items with structured attributes but tiny quantities, such as time. To better learn the vector representation of text information and improve recognition performance, the pre-trained model is adopted for entities with weak characteristics but huge quantities, such as hazardous chemical scenarios. 3. Experimental results show that the proposed method (RT-BBC) can effectively improve the performance of entity recognition using chemical accident information. The remainder of this paper is organized as follows. In Section 2, we summarize the related works in named entity recognition. Section 3 describes in detail the rule templates and pre-trained model we propose.