Σύγκριση μεθόδων

	Αυτοματοποιημένη Βαθμολόγηση Κειμένων (Automated Essay Scoring - AES)	Ταξινόμηση Κειμένου
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1966 (Project Essay Grade); modern deep-learning era from 2019	—
Δημιουργός	Shermis & Burstein (eds.); landmark consolidation 2013; deep-learning era from Devlin et al. 2019	—
Τύπος	Supervised text-regression / text-classification task	Supervised NLP classification task
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	AES, automated writing evaluation, AWE, Otomatik Deneme Puanlaması	text categorization, document classification, topic classification, metin sınıflandırma
Συναφείς	4	4
Σύνοψη	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.