Natural Language Toolkit (NLTK) Guide

Natural Language Toolkit (NLTK) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

Getting Started

Before you begin, make sure you have Python installed on your system. NLTK can be installed using pip:

pip install nltk

Once installed, you can import NLTK in your Python script:

import nltk

Basic Operations

Here are some basic operations you can perform with NLTK:

Tokenization

Tokenization is the process of splitting text into words, sentences, or other meaningful elements called tokens.

from nltk.tokenize import word_tokenize

text = "NLTK is a leading platform for building Python programs to work with human language data."
tokens = word_tokenize(text)
print(tokens)

Part-of-Speech Tagging

Part-of-speech tagging is the process of marking up a word in a text as corresponding to a particular part of speech (e.g., noun, verb, adjective, etc.).

from nltk.tokenize import word_tokenize
from nltk import pos_tag

tokens = word_tokenize(text)
tags = pos_tag(tokens)
print(tags)

Named Entity Recognition

Named entity recognition (NER) is the process of identifying entities in text such as names, locations, organizations, etc.

from nltk.tokenize import word_tokenize
from nltk.tag import ne_chunk

tokens = word_tokenize(text)
ne_tree = ne_chunk(tags)
print(ne_tree)