Abstract
Within the field of Digital Humanities, unsupervised machine learning techniques such as topic modeling have gained a lot of attention over the last years to explore vast volumes of non-structured textual data. Even if this technique is useful to capture recurring themes across document sets which have no metadata, the interpretation of topics has been consistently highlighted in the literature as problematic. This paper proposes a novel method based on Word Embeddings to facilitate the interpretation of terms which constituted a topic, allowing to discern different concepts automatically within a topic. In order to demonstrate this method, the paper uses the “Cabinet Papers” held and digitised by the The National Archives (TNA) of the United Kingdom (UK). After a discussion of our results, based on coherence measures, we provide details of how we can linguistically interpret these results.
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
References
Baroni, M., Dinu, G., Kruszewski, G.: Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pp. 238–247 (2014)
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Blei, D.M., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Blei, D.M., Griffiths, T.L., Jordan, M., Tenenbaum, J.: Hierarchical topic models and the nested Chinese restaurant process. In: Advances in Neural Information Processing Systems 16 (2004)
Blei, D.M., Lafferty, J.D.: Correlated topic models. In: Weiss, Y., Schölkopf, B., Platt, J. (eds.) Advances in Neural Information Processing Systems 18. MIT Press, Cambridge (2006)
Chandler, D.: Semiotics: The Basics, 2nd edn. Routledge, London (2007)
Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Proceedings of the 22nd International Conference on Neural Information Processing Systems, pp. 288–296 (2016)
Deerwester, S.C., Dumais, S.T., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41, 391–407 (1990)
Firth, J.R.: Papers in Linguistics 1934–1951. Oxford, London (1957)
Hengchen, S., Coeckelbergs, M., Van Hooland, S.: Exploring archives with probabilistic models: topic modeling for the valorization of digitised archives of the European Commission. In: IEEE International Conference on Big Data Workshop on Computational Archival Science, Washington D.C., pp. 3245–3249 (2016)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems 2, pp. 3111–3119 (2013)
Newman, D., Lau, J.H., Grieser, K., Baldwin, T.: Automatic evaluation of topic coherence. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 100–108 (2010)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP), pp. 1532–1543 (2014)
Röder, M., Both, A., Hinneburg, A.: Exploring the space of topic coherence measures. In: Proceedings of the Eighth International Conference on Web Search and Data Mining, pp. 399–408 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Coeckelbergs, M., Van Hooland, S. (2020). Concepts in Topics. Using Word Embeddings to Leverage the Outcomes of Topic Modeling for the Exploration of Digitized Archival Collections. In: Mugnaini, R. (eds) Data and Information in Online Environments. DIONE 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-50072-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-50072-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50071-9
Online ISBN: 978-3-030-50072-6
eBook Packages: Computer ScienceComputer Science (R0)