## 4 Discussion  
Developing a specialized domain lexicon is crucial for digitizing the field, enhancing human-computer interaction, and applying deep learning techniques. Despite their high visibility and impact, gas pipeline emergencies currently lack a customized domain lexicon for effective response.  
This paper proposes a method for constructing a gas pipeline emergency lexicon by integrating word embedding and statistical techniques. The main tasks include collecting standards and risk lists, fine tuning a pre-trained model to capture the semantic information, and constructing a domain-specific lexicon.  
Regarding content coverage, the lexicon developed in this study includes investigation reports, laws and regulations, emergency plans, training materials, academic papers, standards and specifications, and news reports, covering various aspects of gas pipeline emergency response.  
Regarding scale, the lexicon boasts advantages in both quantity and diversity of words, with distinct semantic differentiation among different word types, indicating that the proposed model effectively captures domain-specific information.  
Regarding effectiveness, the lexicon enhances the digitization of domain knowledge, enabling rapid search and understanding of relevant information, thereby providing a foundational tool for responding to gas pipeline emergencies.  
Regarding presentation, the lexicon includes domain-specific words, their respective weights, and the degree of similarity between words. In future research, this lexicon can be applied to improve emergency cause identification, result prediction, and knowledge graph construction, which are also our next research focus.  
This lexicon is crucial for responding to gas pipeline emergencies, encompassing early warning and risk identification, accident cause analysis and response, and risk assessment and management. This domain lexicon can be applied to accurately identify causes, automate knowledge graph construction, and quantitatively assess risks.  
The domain-specific lexicon from this study is available upon request. Interested researchers or practitioners are encouraged to contact the authors directly. We are pleased to provide access to the lexicon and support its application in advancing emergency management practices in gas pipeline networks. Incorporating this lexicon into these platforms may enhance critical functions such as risk analysis, decision making, and incident management. The lexicon supports various phases of emergency response, including early warning, real-time incident management, and post-incident analysis. This integration can lead to more precise decision making and effective risk management, potentially strengthening the overall emergency preparedness and response capabilities of organizations dealing with gas pipeline networks.  
Although this study introduces a promising approach to constructing a gas pipeline emergency lexicon, certain limitations exist. Data sources, such as investigation reports and standards, may not fully capture all relevant aspects of gas pipeline incidents, potentially leading to gaps in the lexicon. Additionally, despite efforts to fine tune the BERT  
Figure 8: Two-dimensional visualization of clusters in word embeddings.  
model with domain-specific data, capturing the precise semantic nuances of specialized terms remains challenging, potentially affecting the lexicon's accuracy. Furthermore, the methods used for term extraction and word similarity analysis might not fully address issues like polysemy and synonymy, potentially impacting the reliability of the lexicon. These limitations highlight areas for future research, including expanding data sources, improving model precision, and developing more sophisticated semantic analysis techniques to enhance the lexicon's effectiveness and applicability.