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

Shuang–Hong Yang, Steven P. 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 $\tau$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 this Paper

BibTeX
@InProceedings{pmlr-v15-yang11b, 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}, pages = {823--831}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/yang11b/yang11b.pdf}, url = {https://proceedings.mlr.press/v15/yang11b.html}, 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.} }
Endnote
%0 Conference Paper %T Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality %A Shuang–Hong Yang %A Steven P. Crain %A Hongyuan Zha %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-yang11b %I PMLR %P 823--831 %U https://proceedings.mlr.press/v15/yang11b.html %V 15 %X 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.
RIS
TY - CPAPER TI - Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality AU - Shuang–Hong Yang AU - Steven P. Crain AU - Hongyuan Zha BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-yang11b PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 823 EP - 831 L1 - http://proceedings.mlr.press/v15/yang11b/yang11b.pdf UR - https://proceedings.mlr.press/v15/yang11b.html AB - 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. ER -
APA
Yang, S., Crain, S.P. & Zha, H.. (2011). Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:823-831 Available from https://proceedings.mlr.press/v15/yang11b.html.

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