HDLTex: Hierarchical Deep Learning for Text Classification

Increasingly large document collections require improved 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 performa...

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Bibliographic Details
Published in2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA) pp. 364 - 371
Main Authors Kowsari, Kamran, Brown, Donald E., Heidarysafa, Mojtaba, Jafari Meimandi, Kiana, Gerber, Matthew S., Barnes, Laura E.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Computer architecture
Deep Learning
Deep Neural Networks
Document Classification
Hierarchical Learning
Kernel
Machine learning
Mathematical model
Recurrent neural networks
Support vector machines
Text Mining
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Summary:Increasingly large document collections require improved 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 traditional supervised classifiers has degraded as the number of documents has increased. This is because along with 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.
DOI:10.1109/ICMLA.2017.0-134