Predicting Question Popularity for Community Question Answering
Author:
Affiliation:
1. School of Law, Guangdong University of Technology, Guangzhou 510520, China
2. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Abstract
Funder
National Natural Science Foundation of China
Publisher
MDPI AG
Link
https://www.mdpi.com/2079-9292/13/16/3260/pdf
Reference88 articles.
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2. Kandpal, N., Deng, H., Roberts, A., Wallace, E., and Raffel, C. (2023, January 23–29). Large language models struggle to learn long-tail knowledge. Proceedings of the International Conference on Machine Learning, Honolulu, HI, USA.
3. Wang, Y., Lipka, N., Rossi, R.A., Siu, A., Zhang, R., and Derr, T. (2024, January 20–28). Knowledge graph prompting for multi-document question answering. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.
4. Heterogeneous question answering community detection based on graph neural network;Wu;Inf. Sci.,2023
5. Annamoradnejad, I., and Habibi, J. (2023). Automatic Moderation of User-Generated Content. Encyclopedia of Data Science and Machine Learning, IGI Global.
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