Sammenlign metoder

	Argument Mining	Tekstklassificering
Fagområde	Tekstmining	Tekstmining
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2016	—
Ophavsperson	Lippi & Torroni (state-of-the-art survey)	—
Type	NLP information-extraction task	Supervised NLP classification task
Oprindelig kilde	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.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasser	argumentation mining, argument extraction, Argüman Madenciliği	text categorization, document classification, topic classification, metin sınıflandırma
Relaterede	4	4
Resumé	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.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.