Comparer des méthodes

	Traduction automatique	Étiquetage des parties du discours (POS Tagging)
Domaine	Fouille de textes	Fouille de textes
Famille	Process / pipeline	Process / pipeline
Année d'origine	—	—
Auteur d'origine	—	—
Type	NLP text-to-text generation task	NLP sequence-labelling task
Source fondatrice	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. International Conference on Learning Representations (ICLR).	Ratnaparkhi, A. (1996). A Maximum Entropy Model for Part-Of-Speech Tagging. EMNLP.
Alias	MT, neural machine translation, automatic translation, Makine Çevirisi (Machine Translation)	part-of-speech tagging, grammatical tagging, Sözcük Türü Etiketleme (POS Tagging)
Apparentées	3	3
Résumé	Machine translation (MT) is a natural-language-processing task that automatically converts text in one language into another. Modern MT is built on neural sequence-to-sequence models — the attention mechanism introduced by Bahdanau et al. (2015) and the transformer architecture of Vaswani et al. (2017) — and it widens access to sources for multilingual data analysis and research.	Part-of-speech tagging assigns a grammatical category label — noun, verb, adjective, and so on — to every word in a text. It is a foundational natural-language-processing task, formalised as a statistical model by Ratnaparkhi (1996) and packaged into widely used toolkits such as Stanford CoreNLP (Manning et al., 2014), and it serves as a preliminary step for syntactic analysis and information extraction.