English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Text document classification is an important task for diverse naturallanguage processing based applications. Traditional machine learning approachesmainly focused on reducing dimensionality of textual data to performclassification. This although improved the overall classification accuracy, theclassifiers still faced sparsity problem due to lack of better datarepresentation techniques. Deep learning based text document classification, onthe other hand, benefitted greatly from the invention of word embeddings thathave solved the sparsity problem and researchers focus mainly remained on thedevelopment of deep architectures. Deeper architectures, however, learn someredundant features that limit the performance of deep learning based solutions.In this paper, we propose a two stage text document classification methodologywhich combines traditional feature engineering with automatic featureengineering (using deep learning). The proposed methodology comprises a filterbased feature selection (FSE) algorithm followed by a deep convolutional neuralnetwork. This methodology is evaluated on the two most commonly used publicdatasets, i.e., 20 Newsgroups data and BBC news data. Evaluation results revealthat the proposed methodology outperforms the state-of-the-art of both the(traditional) machine learning and deep learning based text documentclassification methodologies with a significant margin of 7.7% on 20 Newsgroupsand 6.6% on BBC news datasets.

A Robust Hybrid Approach for Textual Document Classification