Using hierarchical document evaluation and weighted associations to find experts in enterprise corpora
Wei Zhang, Jianqing Ma, Yiping Zhong · 2009
An expert finding system enables individuals within a large organization to search for authoritative people in a certain area. Various approaches have been proposed to solve the problem of expert finding and most of them can be classified as either document model or candidate model. In this paper, we propose a novel strategy based on the document model to improve the performance of expert finding system. Since association building and document evaluation are of primary importance to document model, we intend to estimate the strength of association in a better manner than traditional methods by using the count of candidates' occurrences as a factor, improve the document evaluation by employing a hierarchical method which incorporates document structure. What's more, we employ two methods of query expansion based on pseudo relevance feedback and proximity constrains respectively to reformulate original topic to improve the accuracy of expert finding. Finally, we evaluate our methods on the Enterprise corpora provided by Text REtrieval Conference (TREC). Experimental results shows that our strategy brings substantial gains to the expert finding and delivers excellent performance.