Title
Latent semantic indexing and convolutional neural network for multi-label and multi-class text classification
Date Issued
November 2017
Access level
restricted access
Resource Type
conference paper
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
The classification of a real text should not be necessarily treated as a binary or multi-class classification, since the text may belong to one or more labels. This type of problem is called multi-label classification. In this paper, we propose the use of latent semantic indexing to text representation, convolutional neural networks to feature extraction and a single multi layer perceptron for multi-label classification in real text data. The experiments show that the model outperforms state of the art techniques when the dataset has long documents, and we observe that the precision is poor when the size of the texts is small. © 2017 IEEE.
Start page
1
End page
6
Volume
2017-November
Number
7
Language
English
Scopus EID
2-s2.0-85050411727
Source
2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Conference
2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
Source funding
Sponsor(s)
The authors would like to express their gratitude to the National University of San Agustín of Arequipa, CONCYTEC and FONDECYT for the support and funding of this research.
Sources of information:
Directorio de Producción Científica