Abstract
Analyzing short text or documents using topic modeling becomes a popular solutions for the increasing number of documents produced in everyday life. For handling the large amount of documents, many topic modeling algorithms are used e.g. LDA, LSI, pLSI, NMF. In this study, we have used LDA, LSI, NMF and also lexical database wordNet synset for candidate labels in our topics labeling. And finally compare the effectiveness of topic modeling algorithms for short documents. Among those LDA gives the better result in terms of WUP similarity. This study will help to select the proper algorithm for labeling topics and can easily identify the meaning of topics.
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References
Aker, A., Paramita, M., Kurtic, E., Funk, A., Barker, E., Hepple, M., Gaizauskas, R.: Automatic label generation for news comment clusters. In: Proceedings of the 9th International Natural Language Generation Conference, pp. 61–69 (2016)
Basave, A.E.C., He, Y., Xu, R.: Automatic labelling of topic models learned from twitter by summarisation. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 618–624 (2014)
Bhatia, S., Lau, J.H., Baldwin, T.: Automatic labelling of topics with neural embeddings. arXiv preprint arXiv:1612.05340 (2016)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Brown, P.F., Desouza, P.V., Mercer, R.L., Pietra, V.J.D., Lai, J.C.: Class-based n-gram models of natural language. Comput. Linguist. 18(4), 467–479 (1992)
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J.L., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)
Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)
Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 289–296. Morgan Kaufmann Publishers Inc. (1999)
Hossain, S.S., Ul-Hassan, R., Rahman, S.: Polynomial topic distribution with topic modeling for generic labeling. In: Communications in Computer and Information Science, vol. 1046, pp. 413–419. Springer (2019)
Hulpus, I., Hayes, C., Karnstedt, M., Greene, D.: Unsupervised graph-based topic labelling using DBPedia. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 465–474. ACM (2013)
Kou, W., Li, F., Baldwin, T.: Automatic labelling of topic models using word vectors and letter trigram vectors. In: AIRS, pp. 253–264. Springer (2015)
Lau, J.H., Grieser, K., Newman, D., Baldwin, T.: Automatic labelling of topic models. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 1536–1545. Association for Computational Linguistics (2011)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788 (1999)
Mei, Q., Shen, X., Zhai, C.X.: Automatic labeling of multinomial topic models. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 490–499. ACM (2007)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Niu, L., Dai, X., Zhang, J., Chen, J.: Topic2Vec: learning distributed representations of topics. In: 2015 International Conference on Asian Language Processing (IALP), pp. 193–196. IEEE (2015)
Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Sharing clusters among related groups: hierarchical Dirichlet processes. In: Advances in Neural Information Processing Systems, pp. 1385–1392 (2005)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, pp. 133–138. Association for Computational Linguistics (1994)
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Rahman, S. et al. (2020). Assessing the Effectiveness of Topic Modeling Algorithms in Discovering Generic Label with Description. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_18
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DOI: https://doi.org/10.1007/978-3-030-39442-4_18
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