ProtoTopic: Prototypical Network for Few-Shot Medical Topic Modeling
This addresses the challenge of analyzing medical documents with few examples, though it is an incremental improvement in domain-specific topic modeling.
The authors tackled the problem of poor topic modeling performance on medical texts due to limited data by proposing ProtoTopic, a prototypical network-based model, which achieved improved topic coherence and diversity compared to baselines.
Topic modeling is a useful tool for analyzing large corpora of written documents, particularly academic papers. Despite a wide variety of proposed topic modeling techniques, these techniques do not perform well when applied to medical texts. This can be due to the low number of documents available for some topics in the healthcare domain. In this paper, we propose ProtoTopic, a prototypical network-based topic model used for topic generation for a set of medical paper abstracts. Prototypical networks are efficient, explainable models that make predictions by computing distances between input datapoints and a set of prototype representations, making them particularly effective in low-data or few-shot learning scenarios. With ProtoTopic, we demonstrate improved topic coherence and diversity compared to two topic modeling baselines used in the literature, demonstrating the ability of our model to generate medically relevant topics even with limited data.