Re-Assessing the "Classify and Count" Quantification Method
This work provides a more accurate baseline for quantification tasks, which is important for researchers and practitioners in text analysis, though it is incremental as it re-evaluates an existing method.
The paper reassesses the 'Classify and Count' method for quantification, finding that with proper hyperparameter optimization using a quantification loss, it achieves near-state-of-the-art accuracy on three binary sentiment datasets.
Learning to quantify (a.k.a.\ quantification) is a task concerned with training unbiased estimators of class prevalence via supervised learning. This task originated with the observation that "Classify and Count" (CC), the trivial method of obtaining class prevalence estimates, is often a biased estimator, and thus delivers suboptimal quantification accuracy; following this observation, several methods for learning to quantify have been proposed that have been shown to outperform CC. In this work we contend that previous works have failed to use properly optimised versions of CC. We thus reassess the real merits of CC (and its variants), and argue that, while still inferior to some cutting-edge methods, they deliver near-state-of-the-art accuracy once (a) hyperparameter optimisation is performed, and (b) this optimisation is performed by using a true quantification loss instead of a standard classification-based loss. Experiments on three publicly available binary sentiment classification datasets support these conclusions.