Abstract
Topics generated by topic models are typically reproduced as a list of words. To decrease the cognitional overhead of understanding these topics for end-users, we have proposed labeling topics with a noun phrase that summarizes its theme or idea. Using the WordNet lexical database as candidate labels, we estimate natural labeling for documents with words to select the most relevant labels for topics. Compared to WUP similarity topic labeling system, our methodology is simpler, more effective, and obtains better topic labels.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Gildea, D., Hofmann, T.: Topic-based language models using EM. In: Sixth European Conference on Speech Communication and Technology (1999)
Deerwester, S., et al.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)
Salton, G., Michael, J.: Introduction to Modern Information Retrieval. McGill (1983)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann Publishers Inc. (1999)
Denny, M.J., Spirling, A.: Text preprocessing for unsupervised learning: why it matters, when it misleads, and what to do about it. Polit. Anal. 26(2), 168–189 (2018)
Sajid, A., Jan, S., Shah, I.A.: Automatic topic modeling for single document short texts. In: 2017 International Conference on Frontiers of Information Technology (FIT). IEEE (2017)
Wu, Z., Palmer, M.: Verbs semantics and lexical selection. In: Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics (1994)
Makhoul, J., et al.: Performance measures for information extraction. In: Proceedings of DARPA Broadcast News Workshop (1999)
Valle, D., et al.: Extending the Latent Dirichlet Allocation model to presence/absence data: a case study on North American breeding birds and biogeographical shifts expected from climate change. Glob. Change Biol. 24(11), 5560–5572 (2018)
Guo, Y., Barnes, S.J., Jia, Q.: Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent Dirichlet allocation. Tourism Manag. 59, 467–483 (2017)
Feuerriegel, S., Ratku, A., Neumann, D.: Analysis of how underlying topics in financial news affect stock prices using latent Dirichlet allocation. In: 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE (2016)
Pinoli, P., Chicco, D., Masseroli, M.: Latent Dirichlet allocation based on Gibbs sampling for gene function prediction. In: 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. IEEE (2014)
Lienou, M., Maitre, H., Datcu, M.: Semantic annotation of satellite images using latent Dirichlet allocation. IEEE Geosci. Remote Sens. Lett. 7(1), 28–32 (2010)
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Hossain, S.S., Rezwan Ul-Hassan, M., Rahman, S. (2019). Polynomial Topic Distribution with Topic Modeling for Generic Labeling. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_39
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DOI: https://doi.org/10.1007/978-981-13-9942-8_39
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