Exploring Multi-label Stacking in Natural Language Processing

The task of classification with multi-label data is an important research field in Natural Language Processing (NLP). While there have been studies using one-stage multi-label approaches for automatic text classification, there are not many that use two-stages stacking models. In this paper we explo...

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Bibliographic Details
Published inProgress in Artificial Intelligence Vol. 11805; pp. 708 - 718
Main Authors Nunes, Rodrigo Mansueli, Domingues, Marcos Aurélio, Feltrim, Valéria Delisandra
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Classification
Machine learning
Multi-label
Stacking
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Summary:The task of classification with multi-label data is an important research field in Natural Language Processing (NLP). While there have been studies using one-stage multi-label approaches for automatic text classification, there are not many that use two-stages stacking models. In this paper we explored Binary Relevance (BR) classifiers, with J48 and probabilistic Support Vector Machine (SVM), in a two-stage stacking model. We have evaluated our proposal in three textual data sets: The Movie Database (TMDB), Enron email, and EURLEX European legal text. The results showed that by using a two-stage stacking model, we can obtain better results than by using one-stage classifiers.
ISBN:9783030302436
3030302431
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-30244-3_58