Browsing by Author "Upadhya, B.A."

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    Deep Neural Network Models for Question Classification in Community Question-Answering Forums
    (2019) Upadhya, B.A.; Udupa, S.; Sowmya, Kamath S.
    Automatic generation of responses to questions is a challenging problem that has applications in fields like customer support, question-answering forums etc. Prerequisite to developing such systems is a requirement for a methodology that classifies questions as yes/no or opinion-based questions, so that quick and accurate responses can be provided. Performing this classification is advantageous, as yes/no questions can generally be answered using the data that is already available. In the case of an opinion-based or a yes/no question that wasn't previously answered, an external knowledge source is needed to generate the answer. We propose a LSTM based model that performs question classification into the two aforementioned categories. Given a question as an input, the objective is to classify it into opinion-based or yes/no question. The proposed model was tested on the Amazon community question-answer dataset as it is reflective of the problem statement we are trying to solve. The proposed methodology achieved promising results, with a high accuracy rate of 91% in question classification. � 2019 IEEE.
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    Deep Neural Network Models for Question Classification in Community Question-Answering Forums
    (Institute of Electrical and Electronics Engineers Inc., 2019) Upadhya, B.A.; Udupa, S.; Kamath S․, S.S.
    Automatic generation of responses to questions is a challenging problem that has applications in fields like customer support, question-answering forums etc. Prerequisite to developing such systems is a requirement for a methodology that classifies questions as yes/no or opinion-based questions, so that quick and accurate responses can be provided. Performing this classification is advantageous, as yes/no questions can generally be answered using the data that is already available. In the case of an opinion-based or a yes/no question that wasn't previously answered, an external knowledge source is needed to generate the answer. We propose a LSTM based model that performs question classification into the two aforementioned categories. Given a question as an input, the objective is to classify it into opinion-based or yes/no question. The proposed model was tested on the Amazon community question-answer dataset as it is reflective of the problem statement we are trying to solve. The proposed methodology achieved promising results, with a high accuracy rate of 91% in question classification. © 2019 IEEE.