AbstractMassive open online courses (MOOCs) allow students and instructors to discuss through messages posted on a forum. However, the instructors should limit their interaction to the most critical tasks during MOOC delivery so, teacher-led scaffolding activities, such as forum-based support, can be very limited, even impossible in such environments. In addition, students who try to clarify the concepts through such collaborative tools could not receive useful answers, and the lack of interactivity may cause a permanent abandonment of the course. The purpose of this paper is to report the experimental findings obtained evaluating the performance of a text categorization tool capable of detecting the intent, the subject area, the domain topics, the sentiment polarity, and the level of confusion and urgency of a forum post, so that the result may be exploited by instructors to carefully plan their interventions. The proposed approach is based on the application of attention-based hierarchical recurrent neural networks, in which both a recurrent network for word encoding and an attention mechanism for word aggregation at sentence and document levels are used before classification. The integration of the developed classifier inside an existing tool for conversational agents, based on the academically productive talk framework, is also presented as well as the accuracy of the proposed method in the classification of forum posts.
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention
