2017 LatentSemanticIndexingandConvol

• (Quispe et al., 2017) ⇒ Oscar Quispe, Alexander Ocsa, and Ricardo Coronado. (2017). “Latent Semantic Indexing and Convolutional Neural Network for Multi-label and Multi-class Text Classification.” In: Proceedings of IEEE Latin American Conference on Computational Intelligence (LA-CCI 2017). doi:10.1109/LA-CCI.2017.8285711

Subject Headings: Neural Latent Semantic Indexing Algorithm

Cited By

• http://scholar.google.com/scholar?q=%222017%22+Latent+Semantic+Indexing+and+Convolutional+Neural+Network+for+Multi-label+and+Multi-class+Text+Classification

Quotes

Author Keywords

• Convolution; feature extraction; feedforward neural nets; indexing; multilayer perceptrons; pattern classification; text analysis; latent semantic indexing; convolutional neural network; text representation; single multilayer perceptron; text data; multilabel text classification; multiclass text classification; feature extraction; Convolution; Large scale integration; Semantics; Measurement; Indexing; Training; Convolutional neural networks; Latent Semantic Indexing; Convolution Neural Network; Multi Label Classification; Text Classification.

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 LatentSemanticIndexingandConvol	Oscar Quispe Alexander Ocsa Ricardo Coronado			Latent Semantic Indexing and Convolutional Neural Network for Multi-label and Multi-class Text Classification				10.1109/LA-CCI.2017.8285711		2017