Argument mining is an advanced natural language processing (NLP) technique that aims to automatically identify and extract the structures of interpretation and reasoning expressed as arguments. Argument mining helps in recognizing the occurrence of acceptability in a controversial conversation. The significant focus of argument mining is to attain the relationships between the preceding and succeeding arguments and understand the comprehensive coherence of the specific topic.
Discourse structure and opinions are useful factors to understand a controversial conversation.
Discourse structure and opinions highlight the wider knowledge intention of the conversation and interaction between different users in the arguments. Argument mining using discourse structure and opinion from the controversial text conversation provide better language understanding to detect the structure of the arrangements automatically.
• Argumentation mining is the progressing research field in computational linguistics, focusing on analyzing and extracting structured arguments from natural text or unstructured or noisy text.
• Argumentation mining comprehensively understands the content of arguments, linguistic structure, the relationship between arguments, intrinsic conceptual beliefs, and coherence of the specific topic.
• One of the dependable aspects of knowledge in the complete argumentative analysis is discourse structure.
• Discourse structures are indicator patterns that denote the reasoning for the opinion of experts in the arguments.
• Argumentation analysis permits a good discourse analysis and a wider knowledge of the argument intentions and beliefs, and it highlights interactivity between the arguments and their different views.
• The analysis of argumentative discourse connections helps identify the locations and types of the argument constituents and their relationship.
• Argumentation mining with discourse structure and opinion impart effective analysis of arguments by employing deep learning models for further enhancement.