Machine Learning for Natural Language Processing: Techniques and Evaluation

Abstract

The purpose of this research study is to analyse the approaches that are used in machine learning for the solution of natural language processing (NLP) issues. Also, the assessment criteria that are used in the performance analysis of these models are looked at as part of this investigation. We investigate the challenges that come with using machine learning for natural language processing (NLP), including the issue of data bias and the need that these models have the capacity to be explained. Research is conducted into a variety of supervised and unsupervised learning approaches, including as neural networks, topic modelling, and clustering. Also, a number of evaluation metrics, such as accuracy, precision, recall, and F1 score, are discussed. As the research comes to a conclusion, it is highlighted how important it is to find solutions to problems like data bias and the inability to explain results. This is done in order to ensure that the results generated by machine learning models for natural language processing are accurate and unbiased.