Weakly Supervised Short Text Classification for Characterising Video Segments

In this age of life-wide learning, video-based learning has increasingly become a crucial method of education. However, the challenge lies in watching numerous videos and connecting key points from these videos with relevant study domains. This requires video characterization. Existing research on video characterization focuses on manual or automatic methods. These methods either require substantial human resources (experts to identify domain related videos and domain related areas in the videos) or rely on learner input (by relating video parts to their learning), often overlooking the assessment of their effectiveness in aiding learning. Manual methods are subjective, prone to errors and time consuming. Automatic supervised methods require training data which in many cases is unavailable. In this paper we propose a weakly supervised method that utilizes concepts from an ontology to guide models in thematically classifying and characterising video segments. Our research is concentra ted in the health domain, conducting experiments with several models, including the large language model GPT-4. The results indicate that CorEx significantly outperforms other models, while GLDA and Guided BERTopic show limitations in this task. Although GPT-4 demonstrates consistent performance, it still falls behind CorEx. This study offers an innovative perspective in video-based learning, especially in automating the detection of learning themes in video content.