Abstract
Most of the text in the questions of community question–answering systems does not consist of a definite mechanism for the restriction of inappropriate and insincere content. A given piece of text can be insincere if it asserts false claims or assumes something which is debatable or has a non-neutral or exaggerated tone about an individual or a group. In this paper, we propose a pipeline called Deep Refinement which utilizes some of the state-of-the-art methods for information retrieval from highly sparse data such as capsule network and attention mechanism. We have applied the Deep Refinement pipeline to classify the text primarily into two categories, namely sincere and insincere. Our novel approach ‘Deep Refinement’ provides a system for the classification of such questions in order to ensure enhanced monitoring and information quality. The database used to understand the real concept of what actually makes up sincere and insincere includes quora insincere question dataset. Our proposed question classification method outperformed previously used text classification methods, as evident from the F1 score of 0.978.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.