Which NLP task involves assigning grammatical categories or tags to each word in a sentence based on its syntactic function?

Which NLP task involves assigning grammatical categories or tags to each word in a sentence based on its syntactic function?

• A. Text classification
• B. Named Entity Recognition
• C. Part-of-Speech tagging
• D. Dependency parsing

Answer: (C)

Assigning grammatical categories to every word in a sentence based on its syntactic function is Part-of-Speech tagging. This task labels each token with its part of speech—noun, verb, adjective, adverb, and so on—using the surrounding context to decide the appropriate tag. It’s the best fit because it focuses specifically on per-word labeling of grammatical roles rather than classifying the whole text, identifying named entities, or mapping out inter-word dependencies. For example, in a sentence like “She can lead the team,” POS tagging helps determine whether “lead” is a verb or a noun based on how it’s used. These per-word tags also feed into other NLP processes, such as parsing, but the core idea here is the per-word grammatical categorization itself. By contrast, text classification assigns a single label to the entire text, and named entity recognition targets proper names and entities, while dependency parsing concentrates on relationships between words rather than tagging each word with a POS category.

Assigning grammatical categories to every word in a sentence based on its syntactic function is Part-of-Speech tagging. This task labels each token with its part of speech—noun, verb, adjective, adverb, and so on—using the surrounding context to decide the appropriate tag. It’s the best fit because it focuses specifically on per-word labeling of grammatical roles rather than classifying the whole text, identifying named entities, or mapping out inter-word dependencies. For example, in a sentence like “She can lead the team,” POS tagging helps determine whether “lead” is a verb or a noun based on how it’s used. These per-word tags also feed into other NLP processes, such as parsing, but the core idea here is the per-word grammatical categorization itself. By contrast, text classification assigns a single label to the entire text, and named entity recognition targets proper names and entities, while dependency parsing concentrates on relationships between words rather than tagging each word with a POS category.