Argument Mining — Detecting Claims and Their Reasons
Also known as: argumentation mining, argument extraction, Argüman Madenciliği
Argument mining is a natural-language-processing task that automatically detects claims, premises and the argumentative structures that link them within text. Consolidated as a field by Lippi and Torroni's 2016 state-of-the-art survey, it is applied to scientific writing, legal documents and debate analysis to turn free-form argumentation into structured, analysable units.
Key highlights
- Turns free-form argumentation into structured units — claims, premises and the relations between them — that can be searched and analysed.
- Goes beyond topic or sentiment labelling by recovering the rhetorical role each statement plays and how statements connect.
- Applies across high-value domains such as scientific text, legal documents and debate analysis.
Intuition
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How it works
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When to use it
Argument mining fits when you have argumentative text — scientific articles, legal documents, debates, opinion pieces — and a defined argument ontology or annotation scheme to work against. A labelled training set following that scheme is preferred, and a corpus of at least roughly fifty documents is recommended so that the component and relation patterns are learnable. Without a schema or with purely non-argumentative text, the task has nothing to extract.
Strengths & limitations
- Turns free-form argumentation into structured units — claims, premises and the relations between them — that can be searched and analysed.
- Goes beyond topic or sentiment labelling by recovering the rhetorical role each statement plays and how statements connect.
- Applies across high-value domains such as scientific text, legal documents and debate analysis.
- Requires an argument ontology or annotation scheme to be defined up front; results are only as coherent as that schema.
- Labelled training data is preferred, and producing reliable argument annotations is costly and expert-intensive.
- It is an intermediate-difficulty task (rated 3 of 5) and needs a corpus of meaningful size — around fifty documents or more.
Common pitfalls
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Applications
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Frequently asked
How is argument mining different from sentiment or text classification?
Sentiment and ordinary text classification assign a single label to a document or sentence. Argument mining instead identifies the rhetorical role of statements — distinguishing claims from premises — and then reconstructs how they connect, producing a structure rather than a flat label.
Do I need labelled training data?
Labelled data following your annotation scheme is strongly preferred. The task can be approached without it, but reliable component and relation extraction is far easier when models are trained on annotated argumentative text.
What is the argument ontology or scheme for?
It defines which component types exist (such as claim and premise) and which relations are allowed between them. Fixing this schema first is what keeps the extracted labels and structures consistent and interpretable.
How much text do I need?
A corpus of at least roughly fifty documents is recommended so that argument component and relation patterns are learnable; very small or non-argumentative corpora leave the model little to work with.
Sources
- 1.Lippi, M. & Torroni, P. (2016). Argumentation Mining: State of the Art and Emerging Trends. ACM Transactions on Internet Technology, 16(2), Article 10, 1-25.
- 2.Stede, M. & Schneider, J. (2018). Argumentation Mining. Morgan & Claypool.ISBN 9781681731919
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Cite this page
ScholarGate. (2026, June 1). Argument Mining. ScholarGate. https://scholargate.app/text-mining/argument-mining