Unsupervised Semantic Generative Adversarial Networks for Expert Retrieval
Shangsong Liang · 2019
Sources in computer-based collaborative systems such as webpages can help employees to connect and cooperate with each other. It is natural to enable the systems to look not only for documents but also for experts. In this paper, we study the problem of expert retrieval in enterprise corpora: given a topic, also known as query containing a set of words, identify a rank list of candidate experts who have expertise on the topic. To tackle the problem, we propose an unsupervised semantic two-player minimax game, i.e., our unsupervised semantic generative adversarial networks (USGAN). Unlike almost all the previous generative adversarial networks-based algorithms that require ground truth training data, our USGAN is an unsupervised semantic expert retrieval algorithm that consists of a discriminative network and a generative network aiming at capturing the representations of words and experts in an unsupervised way. Candidates that have similar semantic representations to that of the topic are retrieved as relevant to the topic. Our USGAN would provide inspiration on how to extend the standard GAN and its variants by unsupervised ways to address other retrieval tasks where labelled data are missing. Experimental results on public datasets validate the effectiveness of the proposed expert retrieval algorithm.