Unstructured medical records boast an abundance of information that could greatly facilitate medical decision-making and improve patient care. With the development of Natural Language Processing (NLP) methodology, the free-text medical data starts to attract more and more research attention. Most existing studies try to leverage the power of such unstructured data using Machine Learning algorithms, which would usually require a relatively large training set, and high computational capacity. However, when faced with a smaller-scale project, opting for an alternative approach may be more effective and practical. This project proposes an efficient and light-weight rule-based approach to categorize dental diagnosis data. It not only fills the void of dental records in the medical free-text processing area, but also demonstrates that with expertly designed research structure and proper implementation, simple method could achieve our study goal very competently.