Automatic argumentation mining and the role of stance and sentiment
Argumentation mining is a subfield of Computational Linguistics that aims (primarily) at automatically finding
arguments and their structural components in natural language text. We provide a short introduction to this field, intended for an
audience with a limited computational background. After explaining the subtasks involved in this problem of deriving the structure
of arguments, we describe two other applications that are popular in computational linguistics: sentiment analysis and stance
detection. From the linguistic viewpoint, they concern the semantics of evaluation in language. In the final part of the paper, we
briefly examine the roles that these two tasks play in argumentation mining, both in current practice, and in possible future
systems.
Article outline
- 1.Introduction
- 2.Some applications of argumentation mining in text
- 3.Representing the structure of argumentation
- 4.Argumentation mining: Subtasks
- 4.1Find claims
- 4.2Find premises
- 4.3Build a representation of argumentation structure
- 5.Capturing evaluation: Automatic stance detection and sentiment analysis
- 5.1Stance detection
- 5.2Sentiment analysis
- 6.Sentiment and stance in argumentation mining
- 7.Conclusions
- Acknowledgements
- Notes
-
References
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