# Structure-Aware and Format-Enhanced Transformer for Accident Report Modeling
## 5 Experiments  
### Dataset  
To evaluate the effectiveness of the proposed SAFE-Transformer model, we constructed a dedicated dataset for accident investigation report text mining, referred to as the Accident Investigation Corpus (AIC). The data sources span the Ministry of Emergency Management of China, various local emergency management bureaus, and regional government information disclosure platforms. A total of 1632 accident investigation reports issued between 2010 and 2024 were collected.  
The dataset covers five high-risk industries: energy, construction and housing, transportation, petrochemical, and industrial trade. It includes diverse accident types such as falls from height, electric shock, mechanical injury, and struck-by-object incidents. After data cleansing, the original reports retain key sections including accident overview, incident process, cause analysis, evidence description, responsibility attribution, corrective measures, and legal basis. Specifically, data cleansing involved removing non-textual elements--including CSS formatting codes, XML markup tags, and page layout coordinates--while preserving only plain textual content and essential logical symbols such as parentheses, enumerations, and punctuation marks, ensuring textual integrity and semantic consistency.  
On average, each report contains approximately 3042 Chinese characters and exhibits complex and varied document hierarchies. Heading formats span six structural types, including Chinese ordinals, Arabic numerals, and Roman numerals. The directory structures within the reports reflect significant variability across industries and regions. As such, the dataset is characterized by high textual density and intricate structural complexity.  
Annotation was conducted collaboratively by three annotators with safety engineering expertise and two specialists in natural language processing. Based on practical needs in accident report text mining, we defined nine accident intelligence labels, as detailed in Table 2. These labels are distributed across various body paragraphs within the reports.  
To align with the hierarchical structure of accident reports, the annotation team developed a set of regular expression rules \(\mathcal{R}=\{R_{1},R_{2},\ldots,R_{L}\}\) for automatically identifying and matching paragraph-level identifiers. Each rule \(R_{l}\) corresponds to a hierarchical level \(l\), detects the associated symbolic format \(v_{l}\), converts it into a standardized encoding, and records the complete hierarchical tree path (e.g., \"\(1.2\to 1.2.1\to\) (a)\").  
Annotation was conducted on all 1632 reports. On average, each report contained 62 text-level labels and 24 structural nodes, yielding a total of 12,358 annotated documents. To ensure annotation reliability, inter-annotator agreement was assessed using [PERSON]'s \(\alpha\) coefficient, which reached 0.89 (exceeding the commonly accepted threshold of 0.8), indicating strong annotation consistency. Figure 4 shows an example of the original report structure and its annotated counterpart.  
The annotated dataset was randomly split using a 7:2:1 ratio into training (8650 documents), validation (2472 documents), and test (1236 documents) sets. Stratified sampling was employed to ensure consistent label distributions across subsets. For rare labels--such as \"L6 Legal Provisions\", which account for only 0.8%--the validation and test sets were augmented to include at least 5% of such low-frequency samples to enhance evaluation of the model's generalization capability.