Skianis, K., Rousseau, F. and Vazirgiannis, M. (2016) Regularizing Text Categorization with Clusters of Words. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, Austin, 1-5 November 2016, 1827-1837.
https://doi.org/10.18653/v1/D16-1188

has been cited by the following article:

• TITLE: A Novel Efficient and Effective Preprocessing Algorithm for Text Classification

  AUTHORS: Lijie Zhu, Difan Luo

  KEYWORDS: Text Classification, Preprocessing, Feature Dimension, Orthogonal Matching Pursuit

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.3, March 13, 2023

  ABSTRACT: Text classification is an essential task of natural language processing. Preprocessing, which determines the representation of text features, is one of the key steps of text classification architecture. It proposed a novel efficient and effective preprocessing algorithm with three methods for text classification combining the Orthogonal Matching Pursuit algorithm to perform the classification. The main idea of the novel preprocessing strategy is that it combined stopword removal and/or regular filtering with tokenization and lowercase conversion, which can effectively reduce the feature dimension and improve the text feature matrix quality. Simulation tests on the 20 newsgroups dataset show that compared with the existing state-of-the-art method, the new method reduces the number of features by 19.85%, 34.35%, 26.25% and 38.67%, improves accuracy by 7.36%, 8.8%, 5.71% and 7.73%, and increases the speed of text classification by 17.38%, 25.64%, 23.76% and 33.38% on the four data, respectively.