%0 Conference Paper %1 Yu:2005 %A Yu, Hong %A Sable, Carl %B Proc. IJCAI'05 Workshop on Knowledge and Reasoning for Answering Questions %D 2005 %K question_classification question_answering biomedical answerability %T Being Erlang Shen: Identifying Answerable Questions %U http://www.uwm.edu/~hongyu/publications.html %X 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.

@inproceedings{Yu:2005, abstract = {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.}, added-at = {2009-08-14T09:16:09.000+0200}, author = {Yu, Hong and Sable, Carl}, biburl = {https://www.bibsonomy.org/bibtex/261167a4bef4dfea2596b14a905ad46e0/diego_ma}, booktitle = {Proc. IJCAI'05 Workshop on Knowledge and Reasoning for Answering Questions}, interhash = {44a3aa191951f053b00ac9f83fd96cf4}, intrahash = {61167a4bef4dfea2596b14a905ad46e0}, keywords = {question_classification question_answering biomedical answerability}, library = {Web (August 2009)}, timestamp = {2009-08-14T09:16:09.000+0200}, title = {Being Erlang Shen: Identifying Answerable Questions}, url = {http://www.uwm.edu/~hongyu/publications.html}, year = 2005 }