  A sensor name, typically an alphanumeric string, encodes the key context
(e.g., function and location) of a sensor needed for deploying smart building
applications. Sensor names, however, are curated in a building vendor-specific
manner using different structures and vocabularies that are often esoteric.
They thus require tremendous manual effort to annotate on a per-building basis;
even to just segment these sensor names into meaningful chunks. In this paper,
we propose a fully automated self-supervised framework, Sensei, which can learn
to segment sensor names without any human annotation. Specifically, we employ a
neural language model to capture the underlying sensor naming structure and
then induce self-supervision based on information from the language model to
build the segmentation model. Extensive experiments on five real-world
buildings comprising thousands of sensors demonstrate the superiority of Sensei
over baseline methods.
