This workflow defines a fully automated web based application that will label your data using active learning and weak supervision. The workflow was designed for business analysts to easily go through documents to be labeled in any number of classes. In each iteration the user labels more documents and the model is trained using the already labeled instances. With every new iteration, the model proposes documents based on a exploration vs exploitation approach. Once the user is happy with the overall potential falling below a certain value, they can exit the loop and export the model to label the remaining instances. Additionally the workflow lets the user defines rules to label instantly a portion of the dataset with a certain condition. These rules provide weak signals for the weak supervision model training. Rules can be updated at any iteration. This workflow is made to be deployed on KNIME WebPortal via KNIME Server.
This is a companion discussion topic for the original entry at https://kni.me/w/NX0zDvT581uP9HmM