The process of labeling each word in a sentence with its appropriate grammatical category to help systems pick the right translation and keep sentence structure clear.
Part-of-Speech (POS) tagging marks each word with its grammar role (like noun, verb, adjective, or adverb) based on both its definition and context. It is a foundational task in Natural Language Processing (NLP), essential for parsing sentences and understanding linguistic structure.
In localization workflows, POS tagging can improve translation quality, especially when integrated into machine translation or AI-assisted tools.
By understanding the role each word plays in a sentence, systems can better choose appropriate translations, manage word order, and reduce ambiguities. This becomes particularly valuable for languages with flexible grammar rules or words that change meaning based on context.
In multilingual content management, tagging improves accuracy in tasks like translation memory matching, glossary enforcement, and auto-suggestion in CAT tools.