Being Erlang Shen: Identifying Answerable Questions

Research has shown that answers do not exist in biomedical corpora for many questions posed by physicians. We have therefore developed a question filtering component that determines whether or not a posed question is answerable. Using 200 clinical questions that have been annotated by physicians to be answerable or unanswerable, we have explored the use of supervised machine-learning algorithms to automatically classify questions into one of these two categories. We also have incorporated semantic features from a large biomedical knowledge terminology. Our results show that incorporating semantic features in general enhances the performance of question classification and the best system is a probabilistic indexing system that achieves an 80.5% accuracy. Our analysis also shows that stop words may play an important role for separating Answerable from Unanswerable.