Highly imbalanced textual datasets continue to pose a challenge for supervised learning models. However, viewing such imbalanced text data as an anomaly detection (AD) problem has advantages for certain tasks such as detecting hate speech, or inappropriate and/or offensive language in large social media feeds. There the unwanted content tends to be both rare and non-uniform with respect to its thematic character, and better fits the definition of an anomaly than a class. Several recent approaches to textual AD use transformer models, achieving good results but with trade-offs in pre-training and inflexibility with respect to new domains. In this paper we compare two linear models within the NMF family, which also have a recent history in textual AD. We introduce a new approach based on an alternative regularization of the NMF objective. Our results surpass other linear AD models and are on par with deep models, performing comparably well even in very small outlier concentrations.

This paper describes a cellular-telephone-based text-to-text translation system developed at Transclick, Inc. The application translates messages bi-directionally in English, French, German, Italian, Spanish and Portuguese. This paper describes design features uniquely suited to hand-held-device based translation systems. In particular, we discuss some of the usability conditions unique to this type of application and present strategies for overcoming usability obstacles encountered in the design phase of the product.