HDLTex: Hierarchical Deep Learning for Text Classification

The continually increasing number of documents produced each year necessitates ever improving information processing methods for searching, retrieving, and organizing text. Central to these information processing methods is document classification, which has become an important application for supervised learning. Recently the performance of these traditional classifiers has degraded as the number of documents has increased. This is because along with this growth in the number of documents has come an increase in the number of categories. This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification. Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex). HDLTex employs stacks of deep learning architectures to provide specialized understanding at each level of the document hierarchy.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Document Classification WOS-11967 HDLTex Accuracy 86.07 # 1
Document Classification WOS-46985 HDLTex Accuracy 76.58 # 1
Document Classification WOS-5736 HDLTex Accuracy 90.93 # 2


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