Intelligent Document Classification Using Neural Networks

Abstract

Subsequent to smart document categorization has become a core operation in contemporary information discovery, online libraries, business information management and big-data analytics. As the amount of unstructured textual information in terms of academic repositories, social media sites, corporate archives and government databases grows exponentially, automated and intelligent classification methods are critical in terms of efficient data organization and knowledge discovery. Although effective in limited situations, traditional rule-based and statistical machine learning methods fail to scale and generalize when faced with semantic ambiguity, contextual differences and high-dimensional feature spaces. Neural networks and especially deep learning models have proven themselves able to extract semantic representations and contextual relationships in text data remarkably. In this paper, the intelligent document classification using neural networks will be studied in a very comprehensive manner in terms of the architecture, features representation, training techniques, and evaluation procedures. The framework proposed combines text preprocessing, embedding, and neural classification models to obtain robust and scalable document classification. An overall experimental study is performed based on benchmark datasets in an attempt to determine the accuracy of classification, the precision, recall, and the computational efficiency. The findings show that neural network methods are very effective compared to traditional methods particularly when dealing with large and complicated document collections. The paper concludes by mentioning practical implications, challenges, and future research directions in the area of neural document classification.