Optimisation Study of Natural Language Processing Algorithms Based on Deep Learning

Abstract

This paper presents a comprehensive optimization study of natural language processing (NLP) algorithms based on deep learning techniques. The research explores various strategies to enhance the performance and efficiency of NLP models, aiming to address the challenges posed by large-scale datasets and complex linguistic structures. Through a systematic review of existing literature and methodologies, this study synthesizes insights into key optimization approaches, including model architecture design, parameter tuning, and data preprocessing techniques. Moreover, it investigates the impact of different optimization strategies on NLP tasks such as sentiment analysis, named entity recognition, and machine translation. By elucidating the strengths and limitations of various optimization techniques, this paper offers valuable insights for researchers and practitioners in the field of deep learning-based NLP.