Titlebook: Learning to Quantify; Andrea Esuli,Alessandro Fabris,Fabrizio Sebastiani Book‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and The Author(s)

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The Case for Quantification,ons and their estimation, dataset shift, and the various subtypes of dataset shift which are relevant to the quantification endeavour. In this chapter we also argue why using classification techniques for estimating class distributions is suboptimal, and we then discuss why learning to quantify has

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Methods for Learning to Quantify, proposed over the years. These methods belong to two main categories, depending on whether they have an aggregative nature (i.e., they require the classification of all individual unlabelled items as an intermediate step) or a non-aggregative nature (i.e., they perform no classification of individu

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The Quantification Landscape, of quantification research, from its beginnings to the most recent quantification-based “shared tasks”; the landscape of quantification-based, publicly available software libraries; visualization tools specifically oriented to displaying the results of quantification-based experiments; and other ta