Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification

  • Víctor Carrera-Trejo Centro de Investigación en Computación
  • Grigori Sidorov Centro de Investigación en Computación
  • Sabino Miranda-Jiménez Centro de Investigación e Innovación en Tecnologías de la Información y Comunicación (INFOTEC)
  • Marco Moreno Ibarra Centro de Investigación en Computación
  • Rodrigo Cadena Martínez UNITEC
Keywords: Multi-label text classification, Reuters-21578, Latent Dirichlet Allocation, Vector Space Model

Abstract

In text classification task one of the main problems is to choose which features give the best results. Various features can be used like words, n-grams, syntactic n-grams of various types (POS tags, dependency relations, mixed, etc.), or a combinations of these features can be considered. Also, algorithms for dimensionality reduction of these sets of features can be applied, like Latent Dirichlet Allocation (LDA). In this paper, we consider multi-label text classification task and apply various feature sets. We consider a subset of multi-labeled files from the Reuters-21578 corpus. We use traditional tf-IDF values of the features and tried both considering and ignoring stop words. We also tried several combinations of features, like bigrams and unigrams. We also experimented with adding LDA results into Vector Space Models as new features. These last experiments obtained the best results.

Published
2015-01-18
How to Cite
Carrera-Trejo, V., Sidorov, G., Miranda-Jiménez, S., Moreno Ibarra, M., & Cadena Martínez, R. (2015). Latent Dirichlet Allocation complement in the vector space model for Multi-Label Text Classification. International Journal of Combinatorial Optimization Problems and Informatics, 6(1), 7-19. Retrieved from https://ijcopi.org/index.php/ojs/article/view/58
Section
Articles