Learning When to Classify for Early Text Classification

• Autores: Loyola, Juan Martín; Errecalde, Marcelo Luis; Escalante, Hugo J.; Montes y Gomez, Manuel
• Año de publicación: 2017
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; tratamiento de textos; Clasificación; supervised learning; partial information; decision of the moment
• Institución: Universidad Nacional de La Plata

The problem of classification in supervised learning is a widely studied one. 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 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.
Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).
Red de Universidades con Carreras en Informática (RedUNCI)