HDLTex: Hierarchical Deep Learning for Text Classification

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Authors Laura E. Barnes, Matthew S. Gerber, Donald E. Brown, Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa
Journal/Conference Name Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017
Paper Category
Paper Abstract 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.
Date of publication 2017
Code Programming Language Python
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