Dr. Ramesh Nuthakki
Assistant Professor, Atria Institute of Technology, Bangalore – 560024, India.
Abstract
Natural Language Processing (NLP) is a field of study that focuses on the interaction between computers and human language. It involves developing algorithms and models that enable computers to understand, interpret, and generate natural language, which is the language spoken and written by humans. Machine learning plays a critical role in NLP as it enables computers to learn from data and improve their performance on language-related tasks. NLP tasks such as sentiment analysis, language translation, named entity recognition, and text classification require large amounts of labeled data to train machine learning models. Machine learning algorithms such as neural networks, decision trees, and support vector machines can be used to learn patterns in the data and make accurate predictions on new data. The importance of machine learning in NLP cannot be overstated. Without machine learning, it would be challenging to build accurate and efficient NLP systems that can process and analyze large volumes of text data. Machine learning models have been shown to outperform traditional rule-based approaches in NLP tasks, and their performance continues to improve as more data and better algorithms become available. The combination of NLP and machine learning has led to significant advances in natural language processing, making it possible to build systems that can understand, interpret, and generate human language at a level never before possible.