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Citation author topic model in expert search (2010)
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| Venue: | In COLING |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Tu10citationauthor,
author = {Yuancheng Tu and Nikhil Johri and Dan Roth and Julia Hockenmaier},
title = {Citation author topic model in expert search},
booktitle = {In COLING},
year = {2010}
}
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Abstract
This paper proposes a novel topic model, Citation-Author-Topic (CAT) model that addresses a semantic search task we define as expert search – given a research area as a query, it returns names of experts in this area. For example, Michael Collins would be one of the top names retrieved given the query Syntactic Parsing. Our contribution in this paper is two-fold. First, we model the cited author information together with words and paper authors. Such extra contextual information directly models linkage among authors and enhances the author-topic association, thus produces more coherent author-topic distribution. Second, we provide a preliminary solution to the task of expert search when the learning repository contains exclusively research related documents authored by the experts. When compared with a previous proposed model (Johri et al., 2010), the proposed model produces high quality author topic linkage and achieves over 33 % error reduction evaluated by the standard MAP measurement. 1
Keyphrases
expert search citation author topic model novel topic model top name paper author coherent author-topic distribution cited author information extra contextual information query syntactic parsing semantic search task repository contains error reduction high quality author topic linkage research related document michael collins research area preliminary solution author-topic association standard map measurement
