  Natural Language Processing technology has advanced vastly in the past
decade. Text processing has been successfully applied to a wide variety of
domains. In this paper, we propose a novel framework, Text Based
Classification(TBC), that uses state of the art text processing techniques to
solve classification tasks on tabular data. We provide a set of controlled
experiments where we present the benefits of using this approach against other
classification methods. Experimental results on several data sets also show
that this framework achieves comparable performance to that of several state of
the art models in accuracy, precision and recall of predicted classes.
