In this paper, we present the Polylingual Labeled Topic Model, a model which combines the characteristics of the existing Polylingual Topic Model and Labeled LDA. The model accounts for multiple languages with separate topic distributions for each language while restricting the permitted topics of a document to a set of predefined labels. We explore the properties of the model in a two-language setting on a dataset from the social science domain. Our experiments show that our model outperforms LDA and Labeled LDA in terms of their held-out perplexity and that it produces semantically coherent topics which are well interpretable by human subjects.
@article{arxiv.1507.06829,
title = {The Polylingual Labeled Topic Model},
author = {Lisa Posch and Arnim Bleier and Philipp Schaer and Markus Strohmaier},
journal= {arXiv preprint arXiv:1507.06829},
year = {2017}
}
Comments
Accepted for publication at KI 2015 (38th edition of the German Conference on Artificial Intelligence)