Identifying Semantically Deviating Outlier Documents

A document outlier is a document that substantially deviates in semantics from the
majority ones in a corpus. Automatic identification of document outliers can be valuable
in many applications, such as screening health records for medical mistakes. In
this paper, we study the problem of mining semantically deviating document outliers
in a given corpus. We develop a generative model to identify frequent and characteristic
semantic regions in the word embedding space to represent the given corpus,
and a robust outlierness measure which is resistant to noisy content in documents.
Experiments conducted on two real-world textual data sets show that our method can
achieve an up to 135% improvement over baselines in terms of recall at top-1% of
the outlier ranking.