## 2 Background
### Natural Language Processing in Governments  
As part of this study's focus on the application of NLP in government, a literature analysis was conducted to determine the current level of research on using NLP to improve the quality of government work. NLP is a subfield of computer science and AI [33]; in other words, it is similar to machine learning, computer vision, and others. NLP is also one of the fundamental technologies of AI. Since AI has been widely employed in governments for a while, a preliminary survey of the literature reveals that earlier works have mainly concentrated on comprehending and describing the status of AI in public administration rather than concentrating on a particular technology. For example, [PERSON] and others [1] conducted and introduced a systematic review of AI use in government. They concluded that multidisciplinary, theory-driven research on AI use in public governance is required. There is additional research that discusses the state of the art in current research, looks at the challenges of implementing AI in the public sector, and presents the impacts of some of the major policy initiatives throughout the world in relation to AI in government [34; 35]. Researchers discovered a rising trend of interest in AI in the public sector in 2019, with India and the US being the two most active countries [36].  
In terms of NLP, it is hard to find any review or summary research about NLP use in governments. Even so, as mentioned in the introduction section, various NLP approaches have been introduced to the public sector: a method was proposed to automatically construct a large-scale annotated dataset for the classification of government documents using an information retrieval approach [6]. For the demands of public administration, [PERSON] [37] presented a chatbot framework that can respond to all types of questions about the services and offices that are provided. Moreover, the framework can facilitate complex dialogues and encourage citizens by providing hints and recommendations after improvement [38]. Similar to chatbots, [PERSON] and others [2] proposed a question-answering (QA) system that successfully managed ambiguous questions by integrating retrieval methods, task trees, and a rule-based strategy. For most queries about German government services, it provided high-quality responses. In order to improve e-governmentmanagement, [PERSON] [39] identified latent social networks through the study of user comments via opinion and text mining algorithms. Furthermore, NLP has also been applied to assess public opinion and give government feedback or assist the public sector in improving pertinent policies [8; 40; 41; 42]. Especially during the COVID-19 period, many researchers have used data from social media platforms such as Twitter and Facebook to extract comments or posts from the public to identify various insights [43].  
In conclusion, there have been numerous studies on AI applications in governments, but the status quo of the research on NLP applications in governments presents a gap. NLP technologies are being used in governments increasingly and diversely, and what is missing from the past studies is a comprehensive understanding and structured analysis of the applications of NLP in government and the public sector. Therefore, this study aims to analyse and summarise the existing NLP literature in the context of governance and public administration, which is deemed necessary and informative for relevant researchers.