What is it about?
Over the last couple of decades, community question-answering sites (CQAs) have been a topic of much academic interest. Scholars have often leveraged traditional machine learning (ML) and deep learning (DL) to explore the ever-growing volume of content that CQAs engender. To clarify the current state of the CQA literature that has used ML and DL, this paper reports a systematic literature review. The goal is to summarise and synthesise the major themes of CQA research related to (i) questions, (ii) answers and (iii) users.
Featured Image
Photo by Markus Spiske on Unsplash
Why is it important?
New CQA research directions are proposed based on the use of ML and DL.
Read the Original
This page is a summary of: Analysis of community question‐answering issues via machine learning and deep learning: State‐of‐the‐art review, CAAI Transactions on Intelligence Technology, May 2022, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/cit2.12081.
You can read the full text:
Contributors
The following have contributed to this page
