Meta-learning of Text Classification Tasks

Abstract

A text mining characterization is proposed consisting of a set of meta-features, unlike previous meta-learning approaches, some of them are extracted directly from raw text. Such novel description is useful for comparing text mining tasks and study their differences. The problem of determining the task associated to a text classification dataset is introduced and approached with our characterization. Experimental results on a set of 81 corpora show that the proposed meta-features indeed allow to recognize tasks with acceptable performance using only a few meta-features.