Abstract
In the last few decades, topic models have been extensively used to discover the latent topical structure of large text corpora; however, very little has been done to model the continuation of such topics in the near future. In this paper we present a novel approach for tracking topical changes over time and predicting the topics which would continue in the near future. For our experiments, we used a publicly available corpus of conference papers, since scholarly papers lead the technological advancements and represent an important source of information that can be used to make decisions regarding the funding strategies in the scientific community. The experimental results show that our model outperforms two major baselines for dynamic topic modeling in terms of predictive power.
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
Notes
- 1.
The dataset was downloaded from https://www.kaggle.com/.
References
Bahrainian, S.A., Mele, I., Crestani, F.: Modeling discrete dynamic topics. In: Proceedings of the Symposium on Applied Computing, SAC 2017, pp. 858–865 (2017)
Bahreinian, S.A., Crestani, F.: Towards the next generation of personal assistants: systems that know when you forget. In: Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, ICTIR 2017, pp. 169–176 (2017)
Baum, L.E., Sell, G.R.: Growth transformations for functions on manifolds. Pacific J. Math. 27(2), 211–227 (1968)
Blei, D.M., Lafferty, J.D.: Dynamic topic models. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 113–120 (2006)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. B 39(1), 1–38 (1977)
Do, T.-M.-T., Gatica-Perez, D.: By their apps you shall understand them: mining large-scale patterns of mobile phone usage. In: Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia, MUM 2010, pp. 27:1–27:10 (2010)
Griffiths, T.L., Steyvers, M.: Finding scientific topics. In: Proceedings of the National Academy of Sciences (2004)
Kalman, R.E.: A new approach to linear filtering and prediction problems. Trans. ASME-J. Basic Eng. 82(Series D), 35–45 (1960)
Mele, I., Bahrainian, S.A., Crestani, F.: Linking news across multiple streams for timeliness analysis. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, pp. 767–776 (2017)
Rabiner, L.R.: A tutorial on hidden markov models and selected applications in speech recognition. In: Readings in Speech Recognition (1990)
Talley, E.M., Newman, D., Mimno, D., Herr II, B.W., Wallach, H.M., Burns, G.A., Leenders, A.M., McCallum, A.: Database of NIH grants using machine-learned categories and graphical clustering. Nat. Methods 8(6), 443–444 (2011)
Wang, C., Blei, D., Heckerman, D.: Continuous time dynamic topic models. In: Proceedings of Uncertainty in Artificial Intelligence (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bahrainian, S.A., Mele, I., Crestani, F. (2018). Predicting Topics in Scholarly Papers. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-76941-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-76940-0
Online ISBN: 978-3-319-76941-7
eBook Packages: Computer ScienceComputer Science (R0)