Using transfer learning to increase performance of multinomial text classification on documents of different municipalities

This thesis explores transfer learning techniques, in particular TrAdaBoost, for multinomial text classification on municipal document collections. Because each municipality uses different terminology and stylistic conventions, naive models trained on one city often fail on another. The work studies how knowledge can be transferred between domains to improve robustness while using relatively simple models such as Naive Bayes.