Módszerek összehasonlítása

	Automatikus szövegértékelés	BERT-beágyazások	Szövegosztályozás
Tudományterület	Szövegbányászat	Szövegbányászat	Szövegbányászat
Módszercsalád	Process / pipeline	Process / pipeline	Process / pipeline
Keletkezés éve	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)	2019	—
Megalkotó	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)	Devlin, Chang, Lee & Toutanova (Google AI)	—
Típus	Reference-based NLG evaluation metric suite	Contextual transformer text-representation method	Supervised NLP classification task
Alapmű	Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
Alternatív nevek	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	text categorization, document classification, topic classification, metin sınıflandırma
Kapcsolódó	4	4	4
Összefoglaló	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.