Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3297001.3297018acmotherconferencesArticle/Chapter ViewAbstractPublication PagescodsConference Proceedingsconference-collections
research-article

Where to Post: Routing Questions to Right Community in Community Question Answering Systems

Published: 03 January 2019 Publication History

Abstract

At present, question-answer (QA) sites have become one of the most important sources of information sharing. In order to ease search and categorization, QA sites create communities to discuss a specific topic or interest. As a consequence, a large number of communities have been created in the last few years. A lot of research has been conducted on community QA sites to address various problems including expert identification and tag recommendation. However, an important problem that has been neglected so far is to automatically route a question to the right community. In this paper, we propose a novel word-embedding based method to route a question to the right community. We use syntactic as well as semantic features to characterize a question and community. Although this approach of characterization performs well, it is highly computationally expensive. To deal with this problem, we use topic modeling, which effectively summarizes a community and reduces the computation time. Our experimental results reveal that usage of both syntactic and semantic features helps in question routing and leads to a better community prediction. We evaluate our methods on a well-known question answering system Stack Exchange and show the effectiveness of the proposed method.

References

[1]
Agichtein, E., Castillo, C., Donato, D., Gionis, A., and Mishne, G. Finding high-quality content in social media. In Proceedings of the 2008 international conference on web search and data mining (2008), ACM, pp. 183--194.
[2]
Anderson, A., Huttenlocher, D., Kleinberg, J., and Leskovec, J. Discovering value from community activity on focused question answering sites: A case study of stack overflow. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2012), KDD '12, ACM, pp. 850--858.
[3]
Aslay, Ç., O'Hare, N., Aiello, L. M., and Jaimes, A. Competition-based networks for expert finding. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (2013), ACM, pp. 1033--1036.
[4]
Asuncion, A., Welling, M., Smyth, P., and Teh, Y. W. On smoothing and inference for topic models. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence (2009), AUAI Press, pp. 27--34.
[5]
Baltadzhieva, A., and Chrufała, G. Question quality in community question answering forums: a survey. Acm Sigkdd Explorations Newsletter 17, 1 (2015), 8--13.
[6]
Blei, D. M., Ng, A. Y., and Jordan, M. I. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993--1022.
[7]
Fang, H., Wu, F., Zhao, Z., Duan, X., Zhuang, Y., and Ester, M. Community-based question answering via heterogeneous social network learning. In Thirtieth AAAI Conference on Artificial Intelligence (2016).
[8]
Hu, H., Liu, B., Wang, B., Liu, M., and Wang, X. Multimodal dbn for predicting high-quality answers in cqa portals. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (2013), vol. 2, pp. 843--847.
[9]
Kumar, N., Chandarana, Y., Anand, K., and Singh, M. Using social media for word-of-mouth marketing. In International Conference on Big Data Analytics and Knowledge Discovery (2017), Springer, pp. 391--406.
[10]
Lau, J. H., and Baldwin, T. An empirical evaluation of doc2vec with practical insights into document embedding generation. ACL 2016 (2016), 78.
[11]
Le, Q., and Mikolov, T. Distributed representations of sentences and documents. In International Conference on Machine Learning (2014), pp. 1188--1196.
[12]
Mihaylov, T., and Nakov, P. Semanticz at semeval-2016 task 3: Ranking relevant answers in community question answering using semantic similarity based on fine-tuned word embeddings. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) (2016), pp. 879--886.
[13]
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (2013), pp. 3111--3119.
[14]
Nakov, P. Lluıs marquez, alessandro moschitti, walid magdy, hamdy mubarak, abed alhakim freihat, jim glass, and bilal randeree. 2016. semeval-2016 task 3: Community question answering. In Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval (2016), vol. 16, pp. 525--545.
[15]
Neshati, M., Fallahnejad, Z., and Beigy, H. On dynamicity of expert finding in community question answering. Information Processing & Management 53, 5 (2017), 1026--1042.
[16]
Pirie, W. Spearman rank correlation coefficient. Encyclopedia of statistical sciences (1988).
[17]
Quan, X., Kit, C., Ge, Y., and Pan, S. J. Short and sparse text topic modeling via self-aggregation. In IJCAI (2015), pp. 2270--2276.
[18]
Ravi, S., Pang, B., Rastogi, V., and Kumar, R. Great question! question quality in community q&a. ICWSM 14 (2014), 426--435.
[19]
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., and Chanona-Hernández, L. Syntactic n-grams as machine learning features for natural language processing. Expert Systems with Applications 41, 3 (2014), 853--860.
[20]
Srba, I., Grznar, M., and Bielikova, M. Utilizing non-qa data to improve questions routing for users with low qa activity in cqa. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (2015), ACM, pp. 129--136.
[21]
Tian, Q., Zhang, P., and Li, B. Towards predicting the best answers in community-based question-answering services. In ICWSM (2013).
[22]
Toba, H., Ming, Z.-Y., Adriani, M., and Chua, T.-S. Discovering high quality answers in community question answering archives using a hierarchy of classifiers. Information Sciences 261 (2014), 101--115.
[23]
Wang, L., Cao, Z., de Melo, G., and Liu, Z. Relation classification via multi-level attention cnns. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers) (2016), vol. 1, pp. 1298--1307.
[24]
Wang, S., Lo, D., Vasilescu, B., and Serebrenik, A. Entagrec: An enhanced tag recommendation system for software information sites. In Software Maintenance and Evolution (ICSME), 2014 IEEE International Conference on (2014), IEEE, pp. 291--300.
[25]
Xia, X., Lo, D., Wang, X., and Zhou, B. Tag recommendation in software information sites. In Mining Software Repositories (MSR), 2013 10th IEEE Working Conference on (2013), IEEE, pp. 287--296.
[26]
Yang, B., and Manandhar, S. Tag-based expert recommendation in community question answering. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on (2014), IEEE, pp. 960--963.
[27]
Yao, Y., Tong, H., Xie, T., Akoglu, L., Xu, F., and Lu, J. Detecting high-quality posts in community question answering sites. Information Sciences 302 (2015), 70--82.
[28]
Ye, X., Shen, H., Ma, X., Bunescu, R., and Liu, C. From word embeddings to document similarities for improved information retrieval in software engineering. In Proceedings of the 38th international conference on software engineering (2016), ACM, pp. 404--415.
[29]
Yeniterzi, R., and Callan, J. Moving from static to dynamic modeling of expertise for question routing in cqa sites. In ICWSM (2015), pp. 702--705.
[30]
Zeng, D., Liu, K., Lai, S., Zhou, G., and Zhao, J. Relation classification via convolutional deep neural network. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (2014), pp. 2335--2344.
[31]
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., and Zou, W. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC bioinformatics 16, 13 (2015), S8.
[32]
Zhao, Z., Yang, Q., Cai, D., He, X., and Zhuang, Y. Expert finding for community-based question answering via ranking metric network learning. In IJCAI (2016), pp. 3000--3006.
[33]
Zhao, Z., Zhang, L., He, X., and Ng, W. Expert finding for question answering via graph regularized matrix completion. IEEE Transactions on Knowledge and Data Engineering 27, 4 (2015), 993--1004.
[34]
Zhou, G., He, T., Zhao, J., and Hu, P. Learning continuous word embedding with metadata for question retrieval in community question answering. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (2015), vol. 1, pp. 250--259.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
CODS-COMAD '19: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data
January 2019
380 pages
ISBN:9781450362078
DOI:10.1145/3297001
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 January 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Community question answering
  2. community recommendation
  3. data characterization
  4. text mining

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

CoDS-COMAD '19
CoDS-COMAD '19: 6th ACM IKDD CoDS and 24th COMAD
January 3 - 5, 2019
Kolkata, India

Acceptance Rates

CODS-COMAD '19 Paper Acceptance Rate 62 of 198 submissions, 31%;
Overall Acceptance Rate 197 of 680 submissions, 29%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 149
    Total Downloads
  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)1
Reflects downloads up to 04 Oct 2024

Other Metrics

Citations

View Options

Get Access

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media