  Verbs play an important role in the understanding of natural language text.
This paper studies the problem of abstracting the subject and object arguments
of a verb into a set of noun concepts, known as the "argument concepts". This
set of concepts, whose size is parameterized, represents the fine-grained
semantics of a verb. For example, the object of "enjoy" can be abstracted into
time, hobby and event, etc. We present a novel framework to automatically infer
human readable and machine computable action concepts with high accuracy.
