Utilizing artificial intelligence in the application of geotechnical databases is a prudent approach. It is also important to explore how to effectively train artificial intelligence. A common method is to increase the amount of data. However, collecting data for the target variables to be estimated is not easy. On the other hand, the idea of using text information can contribute to increasing the amount of data. There are “one-hot representations” and “distributed representations” for utilizing text. Therefore, this paper proposes a method to supplement insufficient data by using “distributed representations” for soil types and “one-hot representations” for test conditions (UU，CU，C̅U̅，CD) while using raw data for depth, void ratio, and water content as explanatory variables from the geotechnical database. We attempted to estimate the internal friction angle obtained from triaxial compression tests as the target variable. As a result, a correlation coefficient of 0.956 was achieved.