Dhanya Sridhar


Assistant Professor


Curriculum vitae


dhanya.sridhar <at> mila.quebec


DIRO


University of Montreal, Mila


F.04, 6666 Rue St. Urbain



Heterogeneous Supervised Topic Models


Journal article


Dhanya Sridhar, Hal Daumé, D. Blei
Transactions of the Association for Computational Linguistics, 2022

Semantic Scholar DOI
Cite

Cite

APA
Sridhar, D., Daumé, H., & Blei, D. (2022). Heterogeneous Supervised Topic Models. Transactions of the Association for Computational Linguistics.

Chicago/Turabian
Sridhar, Dhanya, Hal Daumé, and D. Blei. “Heterogeneous Supervised Topic Models.” Transactions of the Association for Computational Linguistics (2022).

MLA
Sridhar, Dhanya, et al. “Heterogeneous Supervised Topic Models.” Transactions of the Association for Computational Linguistics, 2022.


Abstract

Abstract Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to both uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the outcome across latent topics. To fit HSTMs, we develop a variational inference algorithm based on the auto-encoding variational Bayes framework. We study the performance of HSTMs on eight datasets and find that they consistently outperform related methods, including fine-tuned black-box models. Finally, we apply HSTMs to analyze news articles labeled with pro- or anti-tone. We find evidence of differing language used to signal a pro- and anti-tone.