We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.

本文引入了一种无监督的辨别模型，用于在线文档检索。我们仅使用文本证据，通过无监督学习分布式词表示来避免显式特征工程。我们将我们的模型与最先进的无监督统计向量空间和概率生成方法进行比较，并发现我们的模型在大多数情况下可以与监督方法匹配，且算法成本较低，因此可以取得与所谓的基于档案的方法相同的检索性能水平。