Abstract
Exploratory search is a paradigm of information retrieval, in which the user’s intention is to learn the subject domain better. To do this the user repeats “query–browse–refine” interactions with the search engine many times. We consider typical exploratory search tasks formulated by long text queries. People usually solve such a task in about half an hour and find dozens of documents using conventional search facilities iteratively. The goal of this paper is to reduce the time-consuming multi-step process to one step without impairing the quality of the search. Probabilistic topic modeling is a suitable text mining technique to retrieve documents, which are semantically relevant to a long text query. We use the additive regularization of topic models (ARTM) to build a model that meets multiple objectives. The model should have sparse, diverse and interpretable topics. Also, it should incorporate meta-data and multimodal data such as n-grams, authors, tags and categories. Balancing the regularization criteria is an important issue for ARTM. We tackle this problem with coordinate-wise optimization technique, which chooses the regularization trajectory automatically. We use the parallel online implementation of ARTM from the open source library BigARTM. Our evaluation technique is based on crowdsourcing and includes two tasks for assessors: the manual exploratory search and the explicit relevance feedback. Experiments on two popular tech news media show that our topic-based exploratory search outperforms assessors as well as simple baselines, achieving precision and recall of about 85–92%.
The original version of this chapter has been revised: The Acknowledgements section has been corrected. The erratum to this chapter is available at https://doi.org/10.1007/978-3-319-71746-3_24
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Andrzejewski, D., Buttler, D.: Latent topic feedback for information retrieval. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2011, pp. 600–608 (2011)
Apishev, M., Koltcov, S., Koltsova, O., Nikolenko, S., Vorontsov, K.: Additive regularization for topic modeling in sociological studies of user-generated texts. In: Sidorov, G., Herrera-Alcántara, O. (eds.) MICAI 2016. LNCS (LNAI), vol. 10061, pp. 169–184. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62434-1_14
Apishev, M., Koltcov, S., Koltsova, O., Nikolenko, S., Vorontsov, K.: Mining ethnic content online with additively regularized topic models. Computacion y Sistemas 20(3), 387–403 (2016)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval: The Concepts and Technology Behind Search (ACM Press Books), vol. 2. Addison-Wesley Professional, Harlow (2011)
Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Frei, O., Apishev, M.: Parallel non-blocking deterministic algorithm for online topic modeling. In: Ignatov, D.I., Khachay, M.Y., Labunets, V.G., Loukachevitch, N., Nikolenko, S.I., Panchenko, A., Savchenko, A.V., Vorontsov, K. (eds.) AIST 2016. CCIS, vol. 661, pp. 132–144. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52920-2_13
Grant, C.E., George, C.P., Kanjilal, V., Nirkhiwale, S., Wilson, J.N., Wang, D.Z.: A topic-based search, visualization, and exploration system. In: FLAIRS Conference, pp. 43–48. AAAI Press, Massachusetts (2015)
Hofmann, T.: Probabilistic latent semantic indexing. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 50–57. ACM, New York (1999)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)
Marchionini, G.: Exploratory search: from finding to understanding. Commun. ACM 49(4), 41–46 (2006)
Rönnqvist, S.: Exploratory topic modeling with distributional semantics. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds.) IDA 2015. LNCS, vol. 9385, pp. 241–252. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24465-5_21
Scherer, M., von Landesberger, T., Schreck, T.: Topic modeling for search and exploration in multivariate research data repositories. In: Aalberg, T., Papatheodorou, C., Dobreva, M., Tsakonas, G., Farrugia, C.J. (eds.) TPDL 2013. LNCS, vol. 8092, pp. 370–373. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40501-3_39
Tan, Y., Ou, Z.: Topic-weak-correlated latent dirichlet allocation. In: 7th International Symposium Chinese Spoken Language Processing (ISCSLP), pp. 224–228 (2010)
Veas, E.E., di Sciascio, C.: Interactive topic analysis with visual analytics and recommender systems. In: 2nd Workshop on Cognitive Computing and Applications for Augmented Human Intelligence, CCAAHI 2015, International Joint Conference on Artificial Intelligence, IJCAI, Buenos Aires, Argentina, July 2015. CEUR-WS.org, Aachen (2015)
Vorontsov, K., Potapenko, A.: Tutorial on probabilistic topic modeling: additive regularization for stochastic matrix factorization. In: Ignatov, D.I., Khachay, M.Y., Panchenko, A., Konstantinova, N., Yavorskiy, R.E. (eds.) AIST 2014. CCIS, vol. 436, pp. 29–46. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12580-0_3
Vorontsov, K.V., Potapenko, A.A.: Additive regularization of topic models. Mach. Learn. 101(1), 303–323 (2015). Special issue on data analysis and intelligent optimization with applications
Vorontsov, K., Potapenko, A., Plavin, A.: Additive regularization of topic models for topic selection and sparse factorization. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds.) SLDS 2015. LNCS (LNAI), vol. 9047, pp. 193–202. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17091-6_14
Vorontsov, K., Frei, O., Apishev, M., Romov, P., Suvorova, M., Yanina, A.: Non-bayesian additive regularization for multimodal topic modeling of large collections. In: Proceedings of the 2015 Workshop on Topic Models: Post-Processing and Applications, pp. 29–37. ACM, New York (2015)
Wei, X., Croft, W.B.: Lda-based document models for ad-hoc retrieval. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2006, pp. 178–185. ACM, New York (2006)
White, R.W., Roth, R.A.: Exploratory Search: Beyond the Query-Response Paradigm. Synthesis Lectures on Information Concepts Retrieval, and Services. Morgan and Claypool Publishers, San Rafael (2009)
Yi, X., Allan, J.: A comparative study of utilizing topic models for information retrieval. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 29–41. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00958-7_6
Acknowledgements
The work was supported by the Ministry of Education and Science of the Russian Federation (project RFMEFI57915X0117).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Ianina, A., Golitsyn, L., Vorontsov, K. (2018). Multi-objective Topic Modeling for Exploratory Search in Tech News. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-71746-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-71746-3_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71745-6
Online ISBN: 978-3-319-71746-3
eBook Packages: Computer ScienceComputer Science (R0)