Abstract
In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in traditional statistical techniques, the need arises for frameworks whereby advanced NLP techniques such as topic modelling may be incorporated within classical methodologies. This paper provides a classical, supervised, statistical learning framework for prediction from text, using topic models as a data reduction method and the topics themselves as predictors, alongside typical statistical tools for predictive modelling. We apply this framework in a Social Sciences context (applied animal behaviour) as well as a Humanities context (narrative analysis) as examples of this framework. The results show that topic regression models perform comparably to their much less efficient equivalents that use individual words as predictors.- Anthology ID:
- W19-2508
- Volume:
- Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, USA
- Editors:
- Beatrice Alex, Stefania Degaetano-Ortlieb, Anna Kazantseva, Nils Reiter, Stan Szpakowicz
- Venue:
- LaTeCH
- SIG:
- SIGHUM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 61–70
- Language:
- URL:
- https://aclanthology.org/W19-2508
- DOI:
- 10.18653/v1/W19-2508
- Cite (ACL):
- Vanessa Glenny, Jonathan Tuke, Nigel Bean, and Lewis Mitchell. 2019. A framework for streamlined statistical prediction using topic models. In Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 61–70, Minneapolis, USA. Association for Computational Linguistics.
- Cite (Informal):
- A framework for streamlined statistical prediction using topic models (Glenny et al., LaTeCH 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/W19-2508.pdf