A Review to unfold the role of Machine Learning Algorithms in Natural Language Processing

Abstract

This research review has been written to explore the role of machine learning algorithms in natural language processing (NLP), a computer field that offers human-like comprehension of virtual text. The main motive of the article is to reveal the participation of machine learning (ML) in NLP and its scope in coherent fields. In this study, the researchers have used a systematic literature review approach to explore the role of ML algorithms in NLP. Highlighting the techniques of ML algorithms as supervised, unsupervised, semi-supervised, and reinforcement methods, this study discloses the connectivity with morphological, semantic, syntactic, pragmatic, and discourse analysis in NLP. The article describes that ML/NLP has immense applications in different fields where several tools of ML/NLP are utilized. The researchers have studied the use of chatbots, text summarization tools, web scrapping, sentimental analysis in the social media stock market, medical field disease detection, and fraud detection. ML’s role and contributions to the progressions are detained in current studies. A distinct comparison has been made between the past present and future of ML in NLP. ML algorithms and applications with examples are disclosed in the classification of logistic regression, SVM, Naive Bayes, K-Nearest Neighbor, and decision tree. Data distortion, interpretation of the research findings, and contextual ambiguity are visibly determined as challenges and discussed in the study. This writing opens up to the recent advancements and advantages in multiple areas correspondingly. The discussions and findings expose the role of ML in opening the gateway to the profound revolutionary search engines, algorithms, and multiple techniques through the development of technology and its evolution to adapt to new language differences, slang, and changes in language use.