Abstract
Document clustering for short texts has received considerable interest. Traditional document clustering approaches are designed for long documents and perform poorly for short texts due to the their sparseness representation. To better understand short texts, we observe that words that appear in long documents can enrich short text context and improve the clustering performance for short texts. In this paper, we propose a novel model, namely DDMAfs, which (1) improves the clustering performance of short texts by sharing structural knowledge of long documents to short texts; (2) automatically identifies the number of clusters; (3) separates discriminative words from irrelevant words for long documents to obtain high quality structural knowledge. Our experiments indicate that the DDMAfs model performs well on the synthetic dataset and real datasets. Comparisons between the DDMAfs model and state-of-the-art short text clustering approaches show that the DDMAfs model is effective.
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References
Bela, A., Frigyik, A., Gupta, M.: Introduction to the dirichlet distribution and related processes. Department of Electrical Engineering, University of Washington (2010)
Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., Freeman, D.: Autoclass: a Bayesian classification system. In: Readings in Knowledge Acquisition and Learning, pp. 431–441. Morgan Kaufmann Publishers Inc., Burlington (1993)
Green, P.J., Richardson, S.: Modelling heterogeneity with and without the dirichlet process. Scand. J. Stat. 28(2), 355–375 (2001)
Hong, L., Davison, B.D.: Empirical study of topic modeling in Twitter. In: Proceedings of the First Workshop on Social Media Analytics, pp. 80–88. ACM (2010)
Hotho, A., Staab, S., Stumme, G.: Wordnet improves text document clustering. In: Proceedings of the SIGIR 2003 Semantic Web Workshop, pp. 541–544 (2003)
Huang, R., Yu, G., Wang, Z., Zhang, J., Shi, L.: Dirichlet process mixture model for document clustering with feature partition. IEEE Trans. Knowl. Data Eng. 25(8), 1748–1759 (2013)
Ishwaran, H., James, L.F.: Gibbs sampling methods for stick-breaking priors. J. Am. Stat. Assoc. 96(453), 161–173 (2001)
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Jin, O., Liu, N.N., Zhao, K., Yu, Y., Yang, Q.: Transferring topical knowledge from auxiliary long texts for short text clustering. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 775–784. ACM (2011)
Phan, X.H., Nguyen, C.T., Le, D.T., Nguyen, L.M., Horiguchi, S., Ha, Q.T.: A hidden topic-based framework toward building applications with short web documents. IEEE Trans. Knowl. Data Eng. 23(7), 961–976 (2011)
Phan, X.H., Nguyen, L.M., Horiguchi, S.: Learning to classify short and sparse text & web with hidden topics from large-scale data collections. In: Proceedings of the 17th International Conference on World Wide Web, pp. 91–100. ACM (2008)
Smyth, P.: Model selection for probabilistic clustering using cross-validated likelihood. Stat. Comput. 10(1), 63–72 (2000)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 990–998. ACM (2008)
Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270. ACM (2010)
Xu, J., Wang, P., Tian, G., Xu, B., Zhao, J., Wang, F., Hao, H.: Short text clustering via convolutional neural networks. In: Proceedings of NAACL-HLT, pp. 62–69 (2015)
Yang, C.L., Benjamasutin, N., Chen-Burger, Y.H.: Mining hidden concepts: using short text clustering and wikipedia knowledge. In: 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 675–680. IEEE (2014)
Yin, J., Wang, J.: A dirichlet multinomial mixture model-based approach for short text clustering. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 233–242. ACM (2014)
Yu, G., Huang, R., Wang, Z.: Document clustering via dirichlet process mixture model with feature selection. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 763–772. ACM (2010)
Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing Twitter and traditional media using topic models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011). doi:10.1007/978-3-642-20161-5_34
Zhong, S.: Semi-supervised model-based document clustering: a comparative study. Mach. Learn. 65(1), 3–29 (2006)
Acknowledgments
This work is supported by Nation Science Foundation of China (Nos. 61462011, 61202089), Introduced Talents Science Projects of Guizhou University (No. 2016050), Major Applied Basic Research Program of Guizhou Province (Grant No. JZ20142001) and the Graduate Innovated Foundation of Guizhou University Project (Nos. 2011015, 2016051).
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Yan, Y. et al. (2017). Improving Document Clustering for Short Texts by Long Documents via a Dirichlet Multinomial Allocation Model. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_47
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