###### Abstract  
Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency-Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks. domain-specific lexicon; pre-trained model; gas pipeline networks; emergency +
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towards intelligent systems, emphasizing the use of multi-source data for risk perception, early warning, risk assessment, intelligent analysis, and decision making [9]. In recent years, numerous critical emergencies in gas pipeline networks have led to significant losses. Investigation reports from various provinces and cities outline several recent events. On 20 September 2023, around 2:45 p.m., Zhongsheng Shengbo Group Co., Ltd. (Anshan, China) was excavating a drainage pipeline at 80 Zhonghua South Road, Rongyu Community, Tiedong District. The construction team accidentally breached a gas pipeline, triggering a flash explosion that damaged the Huaqing Pool bathhouse and injured four people. The incident resulted in direct economic losses estimated at approximately CNY 2.7273 million. On 13 August 2023, around 7:05 a.m., a gas explosion at the intersection of Fenghuang Street and Jiafang Road in Gaomi City, Weifang, caused two deaths and two injuries, with economic losses estimated at approximately CNY 6.94 million. On 15 January 2023, at about 1:25 p.m., an explosion and subsequent fire occurred at Panjin Haoye Chemical Co., Ltd. (Panjin, China) during a pressurized sealing operation on the inlet pipeline of the alkylation unit's water washing tank, resulting in 13 deaths and 35 injuries. This accident caused direct economic damage estimated at approximately CNY 87.99 million. Figure 1 shows the frequency and proportion of these emergencies within all reported gas accidents. Although not exhaustive, data from the Safety Management Committee of the China Urban Gas Association clearly highlight the severity of these emergencies in gas pipeline networks. Emergencies in gas pipeline networks exhibit distinctive characteristics. Typically originating in confined areas, these emergencies can escalate rapidly due to the flammable and explosive nature of gas, causing extensive damage [10]. Managing such events requires broad emergency management skills and specialized technical expertise, especially in detecting gas leaks and repairing pipelines [11]. Effective prevention and response strategies for gas pipeline emergencies rely critically on robust monitoring systems and proactive early warning mechanisms, as timely detection is essential to averting disasters [12]. Developing a domain-specific lexicon is crucial in natural language processing, as it encapsulates essential knowledge of a specific field [13].