Argument Mining: A Survey
By John Lawrence, Chris Reed. Computational Linguistics, 2020
560 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/a2ae7155d94686fe83f26f6d6ca2dfacd16c5e5c
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In Artificial Intelligence
- Saif M. Mohammad
- Svetlana Kiritchenko
- Duy-Tin Vo
- Yulia Tsvetkov
- Mohammad Salameh
- Isabelle Augenstein
- Karolina Stańczak
- Mirella Lapata
Citations in the corpus
- Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “See articles by Lawrence and Reed (2020) and Cabrio and Villata (2018) for surveys on the related area of argument mining.”