  Anomaly detection or outlier detection is a common task in various domains,
which has attracted significant research efforts in recent years. Existing
works mainly focus on structured data such as numerical or categorical data;
however, anomaly detection on unstructured textual data is less attended. In
this work, we target the textual anomaly detection problem and propose a deep
anomaly-injected support vector data description (AI-SVDD) framework. AI-SVDD
not only learns a more compact representation of the data hypersphere but also
adopts a small number of known anomalies to increase the discriminative power.
To tackle text input, we employ a multilayer perceptron (MLP) network in
conjunction with BERT to obtain enriched text representations. We conduct
experiments on three text anomaly detection applications with multiple
datasets. Experimental results show that the proposed AI-SVDD is promising and
outperforms existing works.
