Argument Mining: A Survey

John Lawrence, Chris Reed


Abstract
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.
Anthology ID:
J19-4006
Volume:
Computational Linguistics, Volume 45, Issue 4 - December 2019
Month:
December
Year:
2019
Address:
Cambridge, MA
Venue:
CL
SIG:
Publisher:
MIT Press
Note:
Pages:
765–818
Language:
URL:
https://aclanthology.org/J19-4006
DOI:
10.1162/coli_a_00364
Bibkey:
Cite (ACL):
John Lawrence and Chris Reed. 2019. Argument Mining: A Survey. Computational Linguistics, 45(4):765–818.
Cite (Informal):
Argument Mining: A Survey (Lawrence & Reed, CL 2019)
Copy Citation:
PDF:
https://aclanthology.org/J19-4006.pdf