argument miningargumentative structurefinancial market predictioninference and reasoningnatural language processing
Figures from the paper
Abstract (AI)
Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.
Key Findings
1
Argument mining automatically identifies and extracts inference and reasoning structures expressed as arguments in natural language.
2
Argument mining has potential applications across diverse domains, including financial market prediction and public relations.
3
Automatically extracting deeper reasoning from language remains a significant challenge for argument mining.
4
Modeling argumentative structure reveals both the positions people adopt and the reasons underlying their opinions.
5
The survey reviews foundational techniques and recent advances in argument mining.
Research Object
arguments and their inferential structure expressed in natural language
Research Subject
automatic identification and extraction of argumentative structure, including the positions and reasoning expressed in arguments
Publication Details
Publication Date
2019-10-08
Journal
Publisher
ISSN
Open access PDF
Access Type
Author Information
Download PDF
Subscribe to digest