Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application

TL;DR

This paper reviews the integration of deep learning and machine learning techniques in NLP, emphasizing transformer models, data preprocessing, and addressing challenges like bias and multilingual data for practical AI applications.

Contribution

It provides a comprehensive overview of NLP methods, focusing on transformer-based models, data handling, and ethical considerations for deploying AI solutions.

Findings

Transformer models improve NLP performance

Advanced preprocessing enhances model accuracy

Addressing bias and multilingual challenges is crucial

Abstract

With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intelligence. As artificial intelligence continues to revolutionize fields from healthcare to finance, NLP techniques such as tokenization, text classification, and entity recognition are essential for processing and understanding human language. This paper discusses advanced data preprocessing techniques and the use of frameworks like Hugging Face for implementing transformer-based models. Additionally, it highlights challenges such as handling multilingual data, reducing bias, and ensuring model robustness. By addressing key aspects of data processing and model fine-tuning, this work aims to provide insights into deploying effective and ethically sound AI solutions.