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In this work, we presented an empirical study aimed at analyzing duplicate question detection in stack overflow This study provides a novel way to detect duplicate questions in the stack overfl ow community, posing a daunting problem in natural language processing. For this purpose, we conducted an independent reproduction of two previous.
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One of the problems which jeopardizes that quality is the continuous growth of duplicated questions The result shows that duppredictor. To solve this problem, prior works focused on automatically detecting.
To solve this problem, prior works focused on automatically detecting duplicated.
Rtant step towards improving quality of cqas In this regard, we propose two neural network based architectures for d Plicate question detection on stack overflow To tackle this problem, we explore the use of powerful deep learning techniques, including convolutional neural networks (cnn), recurrent neural networks (rnn) and long.
This review explores the various components of stack overflow's duplicate question detection system, highlighting its strengths, limitations, and potential areas for improvement. Automatic duplicate question detection alleviates labor and effort for users with high reputation Although existing approaches extract textual features to automatically detect duplicate. Stack overflow has become a fundamental element of developer toolset
Such influence increase has been accompanied by an effort from stack overflow community to keep the quality of its.
To examine the benefit of duppredictor, we perform an experiment on a stack overflow dataset which contains a total of more than two million questions
