6 Optimization in Natural Language Processing Models for Enhanced Performance and Efficiency
A. Jasmine Sugil, K. Merriliance · 2025
This chapter provides an in-depth exploration of optimization strategies in natural language processing (NLP), focusing on their part in augmenting model performance besides scalability across different tasks. It begins by outlining the importance of optimization in NLP, highlighting its role in overcoming challenges such as data sparsity, computational complexity, and semantic ambiguity. The chapter then explores various optimization methodologies including parameter optimization, algorithmic optimization, feature engineering and representation learning, deep learning optimization, and domain-specific optimization. Parameter optimizationinvolves refining model parameters, for example, searching grid, search at random, and Bayesian optimization, to improve performance factors like accuracy, precision, and recall across different NLP tasks. Algorithmic optimizations are explored, covering approaches to improve the efficiency and scalability of algorithms for assignments comprising sentiment analysis, named entity recognition, and text classification. Feature engineering involves crafting effective features to capture linguistic patterns and semantic information and integrating representation learning methods like word embeddings and contextualized embeddings to enhance feature representations. Deep learning optimization involves using algorithms like stochastic gradient descent (SGD) and Adam for training neural networks in NLP tasks. Businesses that want to enhance the performance of their NLP functions must prioritize NLP optimization. It includes approaches like as ensemble methods, regularization, hyperparameter tuning, model selection, and data pre-processing. Through NLP model optimization, companies can increase robustness, accuracy, efficiency, and lower risk. Better decision-making, enhanced client experiences, quicker processing times, cheaper costs, and higher NLP application reliability are the results of this. For companies looking to maximize NLP’s potential and innovation in their sectors, NLP optimization is a useful tool.