Argument Mining - Methods and Applications: Exploring methods and applications of argument mining for automatically extracting and analyzing arguments from textual data, such as debates or essays

Abstract

Argument mining is a field of natural language processing (NLP) that focuses on automatically extracting and analyzing arguments from textual data. This paper provides an overview of methods and applications in argument mining, highlighting its importance in understanding and analyzing complex discussions, debates, and essays. We discuss various techniques used in argument mining, including machine learning, deep learning, and natural language processing, along with their strengths and limitations. Additionally, we explore the applications of argument mining in different domains, such as education, law, and public discourse. Through this paper, we aim to provide a comprehensive understanding of argument mining, its methods, applications, and future directions.