Natural Language Processing (NLP) is one of the most exciting areas of Artificial Intelligence today. From chatbots and search engines to spam detection and sentiment analysis, NLP helps machines understand human language.
If you’re just starting out and feel confused by terms like tokenization or lemmatization, this post will give you a clear and gentle introduction.
📌 What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a subfield of Artificial Intelligence that enables computers to understand, analyze, and generate human language.
In simple terms:
NLP allows machines to work with text and speech in a meaningful way.
Real-world applications of NLP
- Chatbots and virtual assistants
- Google Search and autocomplete
- Spam email detection
- Sentiment analysis of reviews
- Language translation
🗺️ A Beginner-Friendly Roadmap to Learn NLP
Before diving into complex models, it’s important to understand how text is processed.
A simple conceptual roadmap
Text Preprocessing
- Tokenization
- Stop words removal
- Stemming
- Lemmatization
Text Representation
- Bag of Words
- TF-IDF
- Word Embeddings
Classical NLP Tasks
- Text classification
- Sentiment analysis
- Named Entity Recognition
Advanced NLP (Later Stage)
- Transformers
- BERT
- GPT
- Large Language Models
🧹 Why Text Preprocessing is Important
Machines don’t understand language like humans do.
Example sentence: "I am learning Natural Language Processing!"
To a machine, this is just a sequence of characters.
Text preprocessing helps convert raw text into a format that machine learning models can understand.
✂️ Tokenization
Tokenization is the process of breaking text into smaller units called tokens.
Example
Sentence:
"I love learning NLP"
After tokenization:
["I", "love", "learning", "NLP"]
Types of tokenization
- Word tokenization
- Sentence tokenization
- Subword tokenization (used in transformers)
🛑 Stop Words
Stop words are commonly used words that usually don’t add much meaning to the text.
Examples:
is, am, are, the, a, an, in, on, and
Why remove stop words?
- They add noise
- They increase dimensionality
- They often don’t help in tasks like classification
🌿 Stemming
Stemming reduces words to their root form by removing suffixes.
- Fast
- Not always linguistically correct
Common stemming algorithms:
- PorterStemmer() : just removes suffix or prefix without context understanding.
- SnowballStemmer() : better than PorterStemmer and supports many languages.
- RegexStemmer() : removes prefix or suffix based on given expression to be removed.