NLTK (Natural Language Toolkit) is a powerful Python library for working with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources like WordNet, along with a suite of text processing libraries for:

• Tokenization

• Text classification

• Stemming and Lemmatization

• POS tagging

• Parsing and Syntax Tree generation

• NER (Named Entity Recognition)

• Language modeling

• Semantic reasoning

📦 Key Features of NLTK

⚙️ Example Code Snippet using NLTK

import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer

# Download required data
nltk.download('punkt')
nltk.download('stopwords')

# Sample text
text = "Natural Language Processing is an exciting field of Artificial Intelligence."

# Tokenization
tokens = word_tokenize(text)

# Stopword removal
stop_words = set(stopwords.words('english'))
filtered = [word for word in tokens if word.lower() not in stop_words]

# Stemming
stemmer = PorterStemmer()
stemmed = [stemmer.stem(word) for word in filtered]

print("Original:", tokens)
print("Filtered:", filtered)
print("Stemmed:", stemmed)

📚 When to Use NLTK?

• When you want control and transparency in NLP tasks

• For educational purposes

• For quick prototyping with classical NLP

For production-level or large-scale NLP, consider using spaCy or transformers which are more optimized.