Learning When to Classify for Early Text Classification

MANUEL MONTES Y GOMEZ; JUAN MARTÍN LOYOLA; MARCELO LUIS ERRECALDE; HUGO JAIR ESCALANTE

Computer Science - CACIC 2017

Springer International Publishing

Año: 2018; p. 24 - 34

The problem of classification is a widely studied one in supervised learning. Nonetheless, there are scenarios that received little attention despite its applicability. One of such scenarios is early text classification, where one needs to know the category of a document as soon as possible. The importance of this variant of the classification problem is evident in tasks like sexual predator detection, where one wants to identify an offender as early as possible. This paper presents a framework for early text classification which highlights the two main pieces involved in this problem: classification with partial information and deciding the moment of classification. In this context, a novel approach that learns the second component (when to classify) and an adaptation of a temporal measurement for multi-class problems are introduced. Results with a classical text classification corpus in comparison against a model that reads the entire documents confirm the feasibility of our approach.