How NLP Systems Use Part-of-Speech Tagging to Decode Grammar

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Part-of-speech (POS) tagging is a foundational natural language processing technique that assigns a grammatical label — such as noun, verb, or adjective — to every word in a sentence. Because the same word can function as different parts of speech depending on context, POS taggers use surrounding words to resolve ambiguity accurately. In NLP, these labels are standardised using Penn Treebank codes like NN, VBZ, and JJ, making grammatical data machine-readable. POS tagging enables several downstream tasks, including named entity recognition, dependency parsing, and word sense disambiguation. By converting unstructured text into structured grammatical information, POS tagging quietly underpins many everyday language applications.

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