השוואת שיטות

	כריית טיעונים	זיהוי סובייקטיביות
תחום	כריית טקסט	כריית טקסט
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2016	—
הוגה השיטה	Lippi & Torroni (state-of-the-art survey)	—
סוג	NLP information-extraction task	NLP text-classification task
מקור מכונן	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.	Wiebe, J., Wilson, T. & Cardie, C. (2005). Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation, 39(2-3), 165-210.
כינויים	argumentation mining, argument extraction, Argüman Madenciliği	subjective vs objective classification, subjectivity classification, Öznellik Tespiti (Subjectivity Detection)
קשורות	4	3
תקציר	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.	Subjectivity detection is a natural-language-processing task that classifies whether a sentence or document conveys objective (neutral information) or subjective (personal opinion, emotion) content. Grounded in the opinion-annotation work of Wiebe and colleagues (2005) and Pang and Lee (2004), it is most often used as a preliminary step before sentiment analysis.