Sentence examples for question classifications from inspiring English sources

As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems.

The question classification accuracy suggests the effectiveness of the method which is promising in the field of open-domain question classification.

Question classification module of a Question Answering System plays a very important role in determining the expectations of the user.

In the literature, incorrect question classification has been cited as one of the major factors for the poor performance of the Question Answering Systems and this emphasizes on the importance of question classification module designing.

Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain.

The experiment results revealed that the average question classification accuracy of QAS when combined with artificial neural network and ontology was 98.76%.

We have compared our results with some standard results reported in the literature and observed a significant improvement in the accuracy of question classification.

In this article, we have proposed a question classification method that exploits the powerful semantic features of the WordNet and the vast knowledge repository of the Wikipedia to describe informative terms explicitly.

Behind the scene, the question classification module is quite impressive, and it will certainly get better over time.

The question templates are defined according to the question classification proposed in [12].

Text-based QA begins with question classification (e.g., yes-no questions, who-questions, what-questions, when-questions, etc)., followed by information retrieval for the identified type of question, followed by narrowing of the search to paragraphs and finally sentences that may contain the answer to the question.