Shallow Learning Versus Deep Learning in Natural Language Processing Applications

The comparison between shallow learning and deep learning methodologies in natural language processing (NLP) applications continues to be a topic of debate and research. This chapter provides an overview of NLP, its history, levels, and tasks. In addition, it reviews and classifies NLP models based on shallow and deep learning methods. Shallow methods, consisting of rule-based and statistical approaches, rely on predefined rules and statistical patterns gleaned from datasets. Shallow learning models, such as support vector machine (SVM), offer simplicity and transparency but may struggle to capture the intricate nuances of language. On the other hand, deep learning architectures, notably convolutional neural networks (CNNs) and recurrent neural networks (RNNs), with deep layers, automatically extract hierarchical features from raw text data. Despite their capability to capture complex relationships within language, deep learning models often require extensive labeled data and computational resources for training. This chapter discusses the strengths and limitations of both shallow and deep learning paradigms for NLP tasks, and future research directions in NLP.