Topic modeling on historical newspapers

Authors:
Tze-I Yang;Andrew J. Torget;Rada Mihalcea
Affiliations:
University of North Texas;University of North Texas;University of North Texas
Venue:
LaTeCH '11 Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities
Year:
2011

Citing 5
Cited 1

Latent dirichlet allocation

The Journal of Machine Learning Research
Probabilistic topic decomposition of an eighteenth-century American newspaper

Journal of the American Society for Information Science and Technology
Incorporating non-local information into information extraction systems by Gibbs sampling

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Studying the history of ideas using topic models

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Evaluating models of latent document semantics in the presence of OCR errors

EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

Historical analysis of legal opinions with a sparse mixed-effects latent variable model

ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we explore the task of automatic text processing applied to collections of historical newspapers, with the aim of assisting historical research. In particular, in this first stage of our project, we experiment with the use of topical models as a means to identify potential issues of interest for historians.