Methoden vergleichen

	Automatisierte Aufsatzbewertung (AES)	Textklassifizierung
Fachgebiet	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline
Entstehungsjahr	1966 (Project Essay Grade); modern deep-learning era from 2019	—
Urheber	Shermis & Burstein (eds.); landmark consolidation 2013; deep-learning era from Devlin et al. 2019	—
Typ	Supervised text-regression / text-classification task	Supervised NLP classification task
Wegweisende Quelle	Shermis, M.D. & Burstein, J. (2013). Handbook of Automated Essay Evaluation. Routledge.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Aliasnamen	AES, automated writing evaluation, AWE, Otomatik Deneme Puanlaması	text categorization, document classification, topic classification, metin sınıflandırma
Verwandt	4	4
Zusammenfassung	Automated Essay Scoring (AES) is a natural-language-processing task in which a computational model assigns scores to student-written essays across dimensions such as grammatical correctness, coherence, content richness, and organisation — replicating, at scale, what a human rater would do. The approach was formalised as a research field by Shermis and Burstein (2013) and has been transformed since 2019 by transformer language models, particularly BERT, which allow AES systems to leverage deep contextual representations of text.	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.