Porównaj metody

	Automatyczna ewaluacja tekstu	Klasyfikacja Tekstu
Dziedzina	Eksploracja tekstu	Eksploracja tekstu
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)	—
Twórca	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)	—
Typ	Reference-based NLG evaluation metric suite	Supervised NLP classification task
Źródło pierwotne	Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Inne nazwy	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics	text categorization, document classification, topic classification, metin sınıflandırma
Pokrewne	4	4
Podsumowanie	Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generation (NLG) outputs — by comparing them to one or more human-written reference texts. Pioneered by Papineni et al. with BLEU in 2002, the field has grown to include n-gram overlap metrics (BLEU, ROUGE) and semantically aware metrics (BERTScore, MoverScore) that capture meaning beyond surface word matches.	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.