Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality

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Shuang–Hong Yang, Steven Crain, Hongyuan Zha ;
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:823-831, 2011.

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 tLDA 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 tLDA with an application to consumer medical informatics. [pdf]

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