POS Tagging
Labelling each word's job
Part-of-speech (POS) tagging assigns every word its grammatical role — noun, verb, adjective, determiner, and so on — based on the word and its context.
It's a classic sequence-labelling task: the tag of each word depends on its neighbours. "book" is a noun in "read a book" but a verb in "book a flight" — only context tells them apart.
Tag a sentence
Watch each word receive its POS tag, including the ambiguous "book" resolved by the words around it.
Common tags
dog, city, London. The "who/what" of a sentence.
run, is, booked. What's happening.
happy, quickly. Describe nouns and verbs.
the, she, on. The grammatical glue.
Why it matters
The lemma of "meeting" depends on whether it's a noun or verb — see Lemmatization.
"ADJ + NOUN" patterns pull out product features, key phrases, and more.
Classic taggers use Hidden Markov Models or CRFs; modern ones use neural sequence models. Libraries like spaCy and NLTK tag out of the box.