  Automatic and accurate classification of items enables numerous downstream
applications in many domains. These applications can range from faceted
browsing of items to product recommendations and big data analytics. In the
online recruitment domain, we refer to classifying job ads to pre-defined or
custom occupation categories as job title classification. A large-scale job
title classification system can power various downstream applications such as
semantic search, job recommendations and labor market analytics. In this paper,
we discuss experiments conducted to improve our in-house job title
classification system. The classification component of the system is composed
of a two-stage coarse and fine level classifier cascade that classifies input
text such as job title and/or job ads to one of the thousands of job titles in
our taxonomy. To improve classification accuracy and effectiveness, we
experiment with various semantic representation strategies such as average W2V
vectors and document similarity measures such as Word Movers Distance (WMD).
Our initial results show an overall improvement in accuracy of Carotene[1].
