HDLTex: Hierarchical Deep Learning for Text Classification (1709.08267v2)

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.

An Overview of Hierarchical Deep Learning for Text Classification (HDLTex)

The field of document classification is essential for organizing the growing volume of textual data. The paper "HDLTex: Hierarchical Deep Learning for Text Classification" presents an innovative approach to this challenge, emphasizing the use of hierarchical deep learning architectures to improve classification accuracy. This essay provides an exploration of the methodologies presented in this work, examines the results, and considers the implications for future applications and research.

Problem Context and Methodology

Traditional document classification methods, such as Naïve Bayes and Support Vector Machines (SVMs), face challenges when dealing with large and complex datasets that require classification into a multitiered hierarchy of categories. As the number of categories increases, the performance of these methods degrades. This paper challenges this limitation by introducing Hierarchical Deep Learning for Text classification (HDLTex), which leverages deep learning to enable hierarchical classification.

HDLTex proposes a model where stacks of deep learning architectures, specifically designed for each level of the hierarchy, are employed to improve accuracy. The paper explores three primary architectures: Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), which can be employed individually or stacked across hierarchical levels. This flexibility allows for specialized learning at each classification stage, potentially outperforming traditional multi-class classification strategies.

Experimental Setup and Results

The research evaluates HDLTex against baseline methods on datasets drawn from the Web of Science, comprising a significant number of categories and documents. The datasets are structured to simulate real-world complexities, encapsulating various scientific fields and sub-fields.

The results are compelling, demonstrating that HDLTex outperforms traditional classification methods in terms of accuracy. In particular, architectures utilizing RNNs at the initial level and combinations of DNNs or CNNs at subsequent levels consistently yielded superior results. The stacked hierarchical models showed marked improvement over single-layer architectures, highlighting the benefit of incorporating hierarchical structures in deep learning models.

For example, in one dataset of over 46,985 documents with 134 hierarchical labels, HDLTex achieved notably higher accuracy rates compared to the baselines, significantly outperforming Naïve Bayes and multi-class SVM approaches.

Implications and Future Directions

The implications of this research are multifaceted, both from practical and theoretical perspectives. Practically, HDLTex can significantly enhance automatic classification systems used in digital libraries, scientific databases, and content management systems, where hierarchical categorization is paramount. This could lead to more efficient information retrieval and organization, benefiting users by facilitating more accurate and relevant content discovery.

Theoretically, HDLTex contributes to the ongoing evolution of deep learning by demonstrating its applicability beyond traditional applications, paving the way for more complex, hierarchical problem domains. Future research could extend this work by exploring additional hierarchical levels, potentially handling even more granular sub-field classifications. Moreover, adapting HDLTex to handle dynamic hierarchies or unlabeled datasets could expand its utility.

In summary, HDLTex represents a substantial advancement in document classification, leveraging hierarchical deep learning to tackle the complexities introduced by large, multifaceted datasets. This research not only refines existing methodologies but also sets the stage for future developments in the domain of text classification.