• Example task: Clean and preprocess a sample text.
• Step 1: Import necessary libraries.
• Step 2: Tokenize text.
• Step 3: Remove stop words.
• Step 4: Apply stemming/lemmatization.
• Python Code Example:

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

# Download necessary NLTK data
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')

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

# Tokenization
tokens = word_tokenize(text)

# Removing stop words
stop_words = set(stopwords.words('english'))
filtered_tokens = [word for word in tokens if word.lower() not in stop_words]

# Stemming
stemmer = PorterStemmer()
stemmed_words = [stemmer.stem(word) for word in filtered_tokens]

# Lemmatization
lemmatizer = WordNetLemmatizer()
lemmatized_words = [lemmatizer.lemmatize(word) for word in filtered_tokens]

print("Original Text:", text)
print("Tokenized Words:", tokens)
print("Filtered Tokens:", filtered_tokens)
print("Stemmed Words:", stemmed_words)
print("Lemmatized Words:", lemmatized_words)