Active Learning for the Text Classification of Rock Climbing Logbook Data

This work applies active learning to the novel problem of automatically classifying user-generated logbook comments, published in online rock climbing forums. These short comments record details about a climber’s experience on a given route. We show that such comments can be successfully classified using a minimal amount of training data. Furthermore, we provide valuable insight into real-world applications of active learning where the cost of annotation is high and the data is imbalanced. We outline the benefits of a model-free approach for active learning, and discuss the difficulties that are faced when evaluating the use of additional training data.