A Review Article for Argumentation Mining of Text through Machine Learning Techniques and Strategies

Abstract

Argumentation Mining (AM), a specialized branch of Natural Language Processing (NLP), which extract arguments from text and mapping out their relationships. While machine learning has been extensively explored for AM sub tasks, there's still a gap in structuring these methods to spot common patterns across different applications. This study, based on a review of 64 research papers, breaks down how AM is applied across various domains ranging from user-generated texts, English texts, speech to debates, legal documents, and scientific or medical texts. Among these, text takes the lead as the most researched area. Particularly Support Vector Machines (SVM), Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN) are some machine learning models that dominates the field. The effectiveness of these models varies depending upon the type of text, excelling in user-generated text where as others perform better with scientific or medical data. The study highlights the need to further explore less-researched areas especially machine learning applications in legal, medical scientific and English texts and critically examine how Large language model and deep learning stacks up against traditional methods. By mapping these insights, the goal is to help researchers pick the right approach for specific AM tasks, ultimately pushing the field forward.