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

The language-gap, for example between lowliteracy laypersons and highly-technical expert documents, 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 the 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 τLDA model explicitly encodes the interplay between topic and technicality hierarchies, providing an effective topic-level bridge between lay and expert documents. We demonstrate the usefulness of τLDA with an application to consumer medical informatics.