Open Collections

González Pizarro, Felipe

University of British Columbia

Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This thesis presents the first systematic and comprehensive evaluation of multimodal topic modeling of documents containing both text and images. In the process, we propose three novel topic modeling solutions and two novel evaluation metrics. Moreover, we focus on one of our models and explore additional techniques to improve the quality of topics, such as incorporating external knowledge. Overall, our evaluation on an unprecedented rich and diverse collection of datasets indicates that all of our models generate coherent and diverse topics. Nevertheless, the extent to which one method outperforms the other depends on the metrics and dataset combinations, which suggests further exploration of combined approaches in the future.

Text

2023-08-14

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85455

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/