TY  - EJOU
AU  - Liu, Yue 
AU  - Guo, Qinglang 
AU  - Yang, Chunyao 
AU  - Liao, Yong 

TI  - TIPS: Tailored Information Extraction in Public Security Using Domain-Enhanced Large Language Model
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - Processing police incident data in public security involves complex natural language processing (NLP) tasks, including information extraction. This data contains extensive entity information—such as people, locations, and events—while also involving reasoning tasks like personnel classification, relationship judgment, and implicit inference. Moreover, utilizing models for extracting information from police incident data poses a significant challenge—data scarcity, which limits the effectiveness of traditional rule-based and machine-learning methods. To address these, we propose TIPS. In collaboration with public security experts, we used de-identified police incident data to create templates that enable large language models (LLMs) to populate data slots and generate simulated data, enhancing data density and diversity. We then designed schemas to efficiently manage complex extraction and reasoning tasks, constructing a high-quality dataset and fine-tuning multiple open-source LLMs. Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%, nearly 30% higher than the base model, significantly reducing error rates. Manual corrections further improved performance by 9.39%. This study demonstrates that combining large-scale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments, offering a new approach for intelligent public security applications.
KW  - Public security; information extraction; large language model; prompt engineering

DO  - 10.32604/cmc.2025.060318