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The question classification accuracy suggests the effectiveness of the method which is promising in the field of open-domain question classification.
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.
Question classification module of a Question Answering System plays a very important role in determining the expectations of the user.
Many studies have been completed on question classification in the open domain, however only limited work focuses on the medical domain.
We have compared our results with some standard results reported in the literature and observed a significant improvement in the accuracy of question classification.
The experiment results revealed that the average question classification accuracy of QAS when combined with artificial neural network and ontology was 98.76%.
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As well, to the best of our knowledge, most of these medical question classifications were designed for literature based question and answering systems.
Furthermore, in the design of the RA-based PSC model of questions classification, several feature selection approaches are investigated.
Therefore, it can be concluded that the system is able to do the questions classification labour in a competitive learning environment.
Since an alien, as well as a citizen, is a "person" for equal protection purposes, a concern for fiscal integrity is no more compelling a justification for the questioned classification in these cases than it was in Shapiro.
"We have a questions classification system based on neural networks and deep learning trained to understand questions," company president and chief strategy officer Mikhail Naumov explained.
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