Abstract
Community Question Answering (CQA) websites provide a platform to ask questions and share their knowledge. Good questions in CQA websites can improve user experiences and attract more users. To the best of our knowledge, a few researches have been studied on the question quality, especially the quality of newly proposed questions. In this work, we consider that a good question is popular and answerable in CQA websites. The community features of questions are extracted automatically and utilized to acquire massive good questions. The text features and asker features of good questions are utilized to train our weakly supervised model based on Convolutional Neural Network to recognize good newly proposed questions. We conduct extensive experiments on the publicly available dataset from StackExchange and our best result achieves F1-score at 91.5%, outperforming the baselines.
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Acknowledgments
This work is sponsored by The Fundamental Theory and Applications of Big Data with Knowledge Engineering under the National Key Research and Development Program of China with grant number 2016YFB1000903, Ministry of Education Innovation Research Team No. IRT 17R86, Innovative Research Group of the National Natural Science Foundation of China (61721002); National Science Foundation of China under Grant Nos. 61672419, 61532004, 61532015, the MOE Research Program for Online Education under Grant No. 2016YB166.
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Zheng, Y. et al. (2017). Quality Prediction of Newly Proposed Questions in CQA by Leveraging Weakly Supervised Learning. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_46
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