Text Preprocessing and Feature Extraction Techniques Enhancing the Accuracy of NLP Models in Industry Applications
Prajna K B, Ashwani Gupta · 2025
This book chapter provides an in-depth exploration of text preprocessing and feature extraction techniques pivotal for enhancing the accuracy of Natural Language Processing (NLP) models in real-world industry applications. Emphasizing key preprocessing steps, including tokenization, normalization, stopword removal, and stemming, the chapter examines their critical role in reducing noise, improving model generalization, and ensuring high-quality input data. Special attention is given to addressing challenges such as ambiguity in stemming and lemmatization, particularly in domain-specific contexts like legal and medical texts. The chapter also investigates innovative strategies in feature extraction, including dimensionality reduction and embedding techniques, to further optimize model performance. By discussing case studies from various industries, including e-commerce, healthcare, and social media, the chapter demonstrates how robust preprocessing pipelines directly contribute to the success of NLP applications. The integration of these techniques leads to the creation of more efficient, scalable, and accurate NLP models for diverse industrial domains.