A document outlier is a document that substantially deviates in semantics from the
\nmajority ones in a corpus. Automatic identification of document outliers can be valuable
\nin many applications, such as screening health records for medical mistakes. In
\nthis paper, we study the problem of mining semantically deviating document outliers
\nin a given corpus. We develop a generative model to identify frequent and characteristic
\nsemantic regions in the word embedding space to represent the given corpus,
\nand a robust outlierness measure which is resistant to noisy content in documents.
\nExperiments conducted on two real-world textual data sets show that our method can
\nachieve an up to 135% improvement over baselines in terms of recall at top-1% of
\nthe outlier ranking.<\/p>\n","protected":false},"excerpt":{"rendered":"
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. 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