  Ontology learning is a critical task in industry, dealing with identifying
and extracting concepts captured in text data such that these concepts can be
used in different tasks, e.g. information retrieval. Ontology learning is
non-trivial due to several reasons with limited amount of prior research work
that automatically learns a domain specific ontology from data. In our work, we
propose a two-stage classification system to automatically learn an ontology
from unstructured text data. We first collect candidate concepts, which are
classified into concepts and irrelevant collocates by our first classifier. The
concepts from the first classifier are further classified by the second
classifier into different concept types. The proposed system is deployed as a
prototype at a company and its performance is validated by using complaint and
repair verbatim data collected in automotive industry from different data
sources.
