A solution for automated column type annotation maps each column contained in a table to a column annotation class. A pre-processor transforms the table into a numerical tensor representation by outputting a sequence of cell tokens for each cell in the table. A table encoder encodes the sequences of cell tokens and a column annotation label for each column into body cell embeddings. A body pooling component processes the body cell embeddings to provide column representations. A classifier classifies the column representations to provide for each column, confidence scores for each column annotation class. The method concludes with comparing the highest confidence score for each column with a threshold, and, if the highest confidence score for each column is above the threshold, annotating each column with the respective column annotation class.