Parts of Speech Tagging Using NLP: Complete Guide

Quick answer: Learn Parts of Speech (POS) tagging in Natural Language Processing, how it works, and how to implement it in Python using NLTK and spaCy.

What is Parts of Speech Tagging?

Parts of Speech (POS) tagging assigns a grammatical category, such as noun, verb or adjective, to each word in a sentence, forming the foundation for many higher level NLP tasks like parsing, named entity recognition and information extraction.

Why POS Tagging Matters

The same word can serve different grammatical roles depending on context. Knowing a word's part of speech helps disambiguate meaning and is a necessary input for tasks that need to understand sentence structure, not just individual words.

"I will book a flight."   -- "book" is a VERB
"I read a good book."      -- "book" is a NOUN

POS Tagging with NLTK

import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

text = "Fireblaze AI School trains students in data science."
tokens = nltk.word_tokenize(text)
tags = nltk.pos_tag(tokens)

print(tags)
# [('Fireblaze', 'NNP'), ('AI', 'NNP'), ('School', 'NNP'), ('trains', 'VBZ'), ...]

POS Tagging with spaCy

import spacy

nlp = spacy.load('en_core_web_sm')
doc = nlp("Fireblaze AI School trains students in data science.")

for token in doc:
    print(token.text, token.pos_)

spaCy tends to be faster and more convenient for production use, while NLTK is often preferred for teaching and detailed linguistic experimentation.

Common POS Tags

How Modern POS Taggers Actually Work

Modern taggers use statistical or neural models trained on large, human-annotated text corpora, learning patterns of which tags typically follow which other tags, rather than relying on a fixed dictionary of word-to-tag mappings, which would fail on ambiguous words entirely.

Practical Use Cases

• Extracting only nouns and proper nouns as candidate keywords from a document

• Filtering adjectives to analyse sentiment-bearing language in reviews

• Serving as a preprocessing step before named entity recognition or dependency parsing

Common Interview Questions

Why is POS tagging harder than it looks?

Many words are ambiguous and change grammatical role depending on context, such as "book" being either a noun or a verb, so accurate tagging requires considering surrounding words, not just the word in isolation.

What is the difference between NLTK and spaCy for POS tagging?

NLTK is often used for teaching and detailed experimentation with different tagging approaches. spaCy is generally faster and better suited to production pipelines, with a simpler, more opinionated API.