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We introduce an unsupervised discriminative model for the task of retrievingexperts in online document collections. We exclusively employ textual evidenceand avoid explicit feature engineering by learning distributed wordrepresentations in an unsupervised way. We compare our model tostate-of-the-art unsupervised statistical vector space and probabilisticgenerative approaches. Our proposed log-linear model achieves the retrievalperformance levels of state-of-the-art document-centric methods with the lowinference cost of so-called profile-centric approaches. It yields astatistically significant improved ranking over vector space and generativemodels in most cases, matching the performance of supervised methods on variousbenchmarks. That is, by using solely text we can do as well as methods thatwork with external evidence and/or relevance feedback. A contrastive analysisof rankings produced by discriminative and generative approaches shows thatthey have complementary strengths due to the ability of the unsuperviseddiscriminative model to perform semantic matching.

Unsupervised, Efficient and Semantic Expertise Retrieval