Part-of-Speech Tagging › Part-of-Speech Tagging

Every word in a sentence carries two kinds of information: its meaning and its grammatical role. Part-of-speech tagging (POS tagging) is the task of automatically assigning each word in a sentence a label that captures that grammatical role. Given "The quick brown fox jumps over the lazy dog," a POS tagger produces output like: DET ADJ ADJ NOUN VERB ADP DET ADJ NOUN.

Why does this seemingly simple annotation matter so much? POS tags give downstream NLP systems a compact, structured summary of sentence structure without the full overhead of a complete syntactic parse. Syntactic parsers use POS tags to prune the search space of possible trees. Named entity recognizers use them to identify entity boundaries, since proper noun sequences (NNP NNP) are strong signals for person or organization names. Question answering systems use verb tags to locate the action a question is asking about. Even modern neural language models, which learn representations end-to-end without explicit POS supervision, benefit from POS auxiliary tasks during training.

The importance of POS tagging extends beyond these direct applications. POS labels function as a compact encoding of local syntactic context. A model that knows a given token is a past-tense verb immediately knows something important: the sentence likely has a predicate at this position, the argument structure involves a subject and possibly an object, and the temporal framing of the clause is in the past. Encoding all of that information through a single two- or three-character tag is extraordinarily economical. For classical NLP pipelines built around hand-crafted feature engineering, POS tags were not just useful; they were indispensable.

This chapter covers the full arc of POS tagging: the tag sets NLP practitioners use, the deep ambiguity problem that makes tagging non-trivial, how both rule-based and statistical approaches tackle that ambiguity, and how we evaluate and implement taggers in practice. By the end you will have working code for both training and applying POS taggers and a clear mental model of where accuracy gains come from.