HDLTex: Hierarchical Deep Learning for Text Classification

TL;DR

HDLTex introduces a hierarchical deep learning approach for text classification, effectively managing large-scale document categorization by leveraging layered architectures to understand document hierarchies.

Contribution

This paper presents HDLTex, a novel hierarchical deep learning framework that improves text classification by capturing hierarchical relationships within documents.

Findings

Enhanced classification accuracy over flat models

Effective handling of large and complex category structures

Layered deep learning architectures improve understanding of document hierarchies

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.