Learning When to Classify for Early Text Classification

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 im...

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Detalles Bibliográficos
Autores principales:	Loyola, Juan Martín, Errecalde, Marcelo Luis, Escalante, Hugo J., Montes y Gomez, Manuel
Formato:	Objeto de conferencia
Lenguaje:	Inglés
Publicado:	2017
Materias:	Ciencias Informáticas tratamiento de textos Clasificación supervised learning partial information decision of the moment
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/63498
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-63498
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas tratamiento de textos Clasificación supervised learning partial information decision of the moment
spellingShingle	Ciencias Informáticas tratamiento de textos Clasificación supervised learning partial information decision of the moment Loyola, Juan Martín Errecalde, Marcelo Luis Escalante, Hugo J. Montes y Gomez, Manuel Learning When to Classify for Early Text Classification
topic_facet	Ciencias Informáticas tratamiento de textos Clasificación supervised learning partial information decision of the moment
description	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.
format	Objeto de conferencia Objeto de conferencia
author	Loyola, Juan Martín Errecalde, Marcelo Luis Escalante, Hugo J. Montes y Gomez, Manuel
author_facet	Loyola, Juan Martín Errecalde, Marcelo Luis Escalante, Hugo J. Montes y Gomez, Manuel
author_sort	Loyola, Juan Martín
title	Learning When to Classify for Early Text Classification
title_short	Learning When to Classify for Early Text Classification
title_full	Learning When to Classify for Early Text Classification
title_fullStr	Learning When to Classify for Early Text Classification
title_full_unstemmed	Learning When to Classify for Early Text Classification
title_sort	learning when to classify for early text classification
publishDate	2017
url	http://sedici.unlp.edu.ar/handle/10915/63498
work_keys_str_mv	AT loyolajuanmartin learningwhentoclassifyforearlytextclassification AT errecaldemarceloluis learningwhentoclassifyforearlytextclassification AT escalantehugoj learningwhentoclassifyforearlytextclassification AT montesygomezmanuel learningwhentoclassifyforearlytextclassification
bdutipo_str	Repositorios
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Learning When to Classify for Early Text Classification

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