Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality
Abstract
The language-gap, for example between low-literacy laypersons and highly-technical experts, is a fundamental barrier for cross-domain knowledge transfer. This paper seeks to close the gap at the thematic level via topic adaptation, i.e., adjusting topical structures for cross-domain documents according to a domain factor such as technicality. We present a probabilistic model for this purpose based on joint modeling of topic and technicality. The proposed $\tau$LDA model explicitly encodes the interplay between topic and technicality hierarchies, providing an effective topic-bridge between lay and expert documents. We demonstrate the usefulness of $\tau$LDA with an application to consumer medical informatics.
Cite
Text
Yang et al. "Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.Markdown
[Yang et al. "Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality." Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.](https://mlanthology.org/aistats/2011/yang2011aistats-bridging/)BibTeX
@inproceedings{yang2011aistats-bridging,
title = {{Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality}},
author = {Yang, Shuang–Hong and Crain, Steven P. and Zha, Hongyuan},
booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
year = {2011},
pages = {823-831},
volume = {15},
url = {https://mlanthology.org/aistats/2011/yang2011aistats-bridging/}
}