An Exhaustive Comparative Study of Machine Learning Algorithms for Natural Language Processing Applications
Year: 2024
Type: conference-paper
Abstract: The past few decades have witnessed an enormous research growth in the field of natural language processing. In this regard, numerous machine learning (ML) algorithms have been applied in different sub-domains of NLP such as speech recognition, text classification, sentiment analysis, etc. Furthermore, their performances have been evaluated using diverse performance metrics. However, a comparative analysis of various ML algorithms in the aforementioned field is a feasible research area to explore. This may efficiently guide future research to precisely focus on the improvement of those particular algorithms that have been found to be more effective based on previous research. Thus, this article provides a comparative analysis regarding the application and effectiveness of different ML algorithms in the field of NLP. Additionally, it highlights the future research direction to be adopted for enhancing the ability of the natural language processing domain.
Authors: Kanwar Mansoor Ali, Talha Ahmed Khan, Syed Mubashir Ali, Asif Aziz, Sharfuddin Ahmed Khan, Sadique Ahmad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404332231