Part Of Speech Tagging

Part Of Speech Tagging

Part of speech tagging assigns each word a grammatical category, such as noun, verb, adjective, or preposition. It is a foundational step that exposes the syntactic structure under a sentence.

The challenge is ambiguity. Many words wear more than one hat. Book is a noun in read the book but a verb in book a flight. The correct tag depends on the surrounding words, so a tagger must reason about context rather than look up each word alone.

Like NER, this is a sequence labeling task with one tag per token. The output stream of tags then feeds parsers, named entity recognizers, and information extractors.

Three families of taggers appear across history:

• Rule based taggers, which apply handcrafted grammar patterns
• Statistical taggers, which learn tag probabilities from labeled corpora
• Neural taggers, which encode context with embeddings and recurrent or transformer layers

Accuracy on well studied languages like English is high, often above ninety seven percent, because the local context usually pins the tag. The hard cases involve rare words, new slang, and genuinely ambiguous constructions.

Part of speech tags remain useful even in the deep learning era as interpretable features and as a sanity check on how a model reads grammar.

Key idea

Part of speech tagging labels each word with its grammatical role, using context to resolve words that can be more than one category.