Abstract
Community Question Answering (CQA) is becoming an increasingly important web service for people to search for expertise and to share their own. With lots of questions being solved, CQA have built a massive, freely accessible knowledge repository, which can provide valuable information for the broader society rather than just satisfy the question askers. It is critically important for CQA services to get high quality answers in order to maximize the benefit of this process. However, people are considered as experts only in their own specialized areas. This paper is concerned with the problem of expert recommendation for a newly posed question, which will reduce the questioner’s waiting time and improve the quality of the answer, so as to improve the satisfaction of the whole community. We propose an approach based on convolutional neural networks (CNN) to resolve this issue. Experimental analysis over a large real-world dataset from Stack Overflow demonstrates that our approach achieves a significant improvement over several baseline methods.
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References
Balog K, Fang Y, Rijke D M, et al. Expertise retrieval. Found Trends Inf Retr, 2012, 6: 127–256
Liu X Y, Croft W B, Koll M. Finding experts in community-based question-answering services. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, Bremen, 2005. 315–316
Li B, King I, Lyu M R. Question routing in community question answering: putting category in its place. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, Glasgow, 2011. 2041–2044
Zhou G, Liu K, Zhao J. Joint relevance and answer quality learning for question routing in community QA. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, Maui, 2012. 1492–1496
Riahi F, Zolaktaf Z, Shafiei M, et al. Finding expert users in community question answering. In: Proceedings of the 21st International Conference on World Wide Web, Lyon, 2012. 791–798
Mandal D P, Kundu D, Maiti S. Finding experts in community question answering services: a theme based query likelihood language approach. In: Proceedings of IEEE International Conference on Advances in Computer Engineering and Applications, Ghaziabad, 2015. 423–427
Yang L, Qiu M, Gottipati S, et al. Cqarank: jointly model topics and expertise in community question answering. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, San Francisco, 2013. 99–108
Yang B, Manandhar S. Tag-based expert recommendation in community question answering. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Beijing, 2014. 960–963
Zhao Z, Zhang L J, He X F, et al. Expert finding for question answering via graph regularized matrix completion. IEEE Trans Knowl Data Eng, 2015, 27: 993–1004
Bengio Y, Ducharme R, Vincent P, et al. A neural probabilistic language model. J Mach Learn Res, 2003, 3: 1137–1155
Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch. J Mach Learn Res, 2011, 12: 2493–2537
Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. In: Proceedings of International Conference on Neural Information Processing Systems, Lake Tahoe, 2013. 3111–3119
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521: 436–444
LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86: 2278–2324
Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, Doha, 2014. 1746–1751
Pal A, Chang S, Konstan J A. Evolution of experts in question answering communities. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media, Dublin, 2012. 274–281
Gao W, Zhou Z H. Dropout Rademacher complexity of deep neural networks. Sci China Inf Sci, 2016, 59: 072104
Dong H L, Wang J, Lin H F, et al. Predicting best answerers for new questions: an approach leveraging distributed representations of words in community question answering. In: Proceedings of the 9th International Conference on Frontier of Computer Science and Technology, Dalian, 2015. 13–18
Pedregosa F, Michel V G, Gramfort A, et al. Scikit-learn: machine learning in Python. J Mach Learn Res, 2012, 12: 2825–2830
Blei D M, Ng A Y, Jordan M I. Latent Dirichlet allocation. J Mach Learn Res, 2003, 3: 993–1022
Du L, Buntine W, Jin H D. A segmented topic model based on the two-parameter Poisson-Dirichlet process. Mach Learn, 2010, 8: 5–19
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61572098, 61632011, 61562080), National Key Research Development Program of China (Grant No. 2016YF-B1001103), and Major Projects of Science and Technology Innovation in Liaoning Province (Grant No. 20151060-21).
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Wang, J., Sun, J., Lin, H. et al. Convolutional neural networks for expert recommendation in community question answering. Sci. China Inf. Sci. 60, 110102 (2017). https://doi.org/10.1007/s11432-016-9197-0
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DOI: https://doi.org/10.1007/s11432-016-9197-0