  Recent progress in AutoML has lead to state-of-the-art methods (e.g.,
AutoSKLearn) that can be readily used by non-experts to approach any supervised
learning problem. Whereas these methods are quite effective, they are still
limited in the sense that they work for tabular (matrix formatted) data only.
This paper describes one step forward in trying to automate the design of
supervised learning methods in the context of text mining. We introduce a meta
learning methodology for automatically obtaining a representation for text
mining tasks starting from raw text. We report experiments considering 60
different textual representations and more than 80 text mining datasets
associated to a wide variety of tasks. Experimental results show the proposed
methodology is a promising solution to obtain highly effective off the shell
text classification pipelines.
