Abstract
With ever increasing number of publication venues and research topics, it is becoming difficult for users to find out topics of interest for conferences or research areas. Although we have many popular topic modeling techniques, we still find that conferences are listing their topics of interest using a manual approach. Topics that are generated by existing topic modeling algorithms are good for text categorization, but they are not ideal for displaying to users because they generate topics that are not so readable and are often redundant. In this paper, we propose a novel technique to generate topics of interest using association mining and natural language processing. We show that the topics of interest that are generated by our technique is much more similar to manually written topics of interest compared to existing topic modeling algorithms. Our results show that the proposed method generates meaningful, interpretable topics, and leads to 13.9% higher precision than existing techniques.
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References
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Bird, C., Devanbu, P., Barr, E., Filkov, V., Nash, A., Su, Z.: Structure and dynamics of research collaboration in computer science. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 826–837. SIAM (2009)
Biryukov, M., Dong, C.: Analysis of computer science communities based on DBLP. In: Lalmas, M., Jose, J., Rauber, A., Sebastiani, F., Frommholz, I. (eds.) ECDL 2010. LNCS, vol. 6273, pp. 228–235. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15464-5_24
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Borgelt, C.: An implementation of the FP-growth algorithm. In: Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp. 1–5. ACM (2005)
Chang, J., Boyd-Graber, J.L., Gerrish, S., Wang, C., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: NIPS, vol. 31, pp. 1–9 (2009)
El-Kishky, A., Song, Y., Wang, C., Voss, C.R., Han, J.: Scalable topical phrase mining from text corpora. VLDB 8(3), 305–316 (2014)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM Sigmod Rec. 29, 1–12 (2000). ACM
Hofmann, T.: Probabilistic latent semantic indexing. In: ACM SIGIR, pp. 50–57. ACM (1999)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. Wiley, London (2009)
Li, W., McCallum, A.: Pachinko allocation: dag-structured mixture models of topic correlations. In: ICML, pp. 577–584. ACM (2006)
Lim, K.W., Chen, C., Buntine, W.: Twitter-network topic model: a full Bayesian treatment for social network and text modeling. In: NIPS 2013 Topic Model Workshop, pp. 1–5 (2013)
Lindsey, R.V., Headden III, W.P., Stipicevic, M.J.: A phrase-discovering topic model using hierarchical Pitman-Yor processes. In: Proceedings of the 2012 Joint Conference on EMNLP and CoNLL, pp. 214–222. ACL (2012)
Liu, Z., Chen, X., Zheng, Y., Sun, M.: Automatic keyphrase extraction by bridging vocabulary gap. In: CoNLL, pp. 135–144. ACL (2011)
Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: ACL (System Demonstrations), pp. 55–60 (2014)
Mei, Q., Cai, D., Zhang, D., Zhai, C.: Topic modeling with network regularization. In: WWW, pp. 101–110. ACM (2008)
Mei, Q., Zhai, C.: A mixture model for contextual text mining. In: SIGKDD, pp. 649–655. ACM (2006)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Paul, M.J., Dredze, M.: Discovering health topics in social media using topic models. PLoS ONE 9(8), e103408 (2014)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: SIGKDD, pp. 990–998. ACM (2008)
Teh, Y.W., Newman, D., Welling, M.: A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. In: NIPS, vol. 6, pp. 1378–1385 (2006)
Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 977–984. ACM (2006)
Wang, X., McCallum, A., Wei, X.: Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: ICDM, pp. 697–702. IEEE (2007)
Yin, Z., Cao, L., Gu, Q., Han, J.: Latent community topic analysis: integration of community discovery with topic modeling. ACM Trans. Intell. Syst. Technol. (TIST) 3(4), 63 (2012)
Zaïane, O.R., Chen, J., Goebel, R.: Mining research communities in bibliographical data. In: Zhang, H., Spiliopoulou, M., Mobasher, B., Giles, C.L., McCallum, A., Nasraoui, O., Srivastava, J., Yen, J. (eds.) SNAKDD/WebKDD -2007. LNCS, vol. 5439, pp. 59–76. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00528-2_4
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Kumar, N., Utkoor, R., Appareddy, B.K.R., Singh, M. (2017). Generating Topics of Interests for Research Communities. 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_34
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DOI: https://doi.org/10.1007/978-3-319-69179-4_34
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