BECAS
LOYOLA Juan Martin
capítulos de libros
Título:
Learning When to Classify for Early Text Classification
Autor/es:
JUAN MART├ŹN LOYOLA; MARCELO LUIS ERRECALDE; HUGO JAIR ESCALANTE; MANUEL MONTES Y GOMEZ
Libro:
Computer Science - CACIC 2017
Editorial:
Springer International Publishing
Referencias:
Año: 2018; p. 24 - 34
Resumen:
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.