A. What is Natural Language Processing (NLP)?
Natural Language Processing is a branch of Artificial Intelligence that allows computers to understand, interpret, and respond to human language (spoken or written).
In simple words, NLP helps machines:
- Read and understand text or speech.
- Translate languages.
- Answer questions.
- Perform tasks using voice commands.
B. Importance of NLP in AI
NLP makes communication between humans and machines possible in a natural way.
Examples of NLP in daily life:
- Chatbots (e.g., customer support bots)
- Voice Assistants (Alexa, Siri, Google Assistant)
- Language Translation (Google Translate)
- Spell check and auto-correct
- Search engines (Google suggestions)
C. How NLP Works
NLP works by converting natural language (English, Hindi, etc.) into machine-readable format, analyzing it, and then converting it back into language the user understands.
It involves:
- Text/Voice Input
- Processing and Understanding
- Generating Response
D. Key Tasks in NLP
| Task Name | Description |
|---|---|
| Text Classification | Categorizing text (e.g., spam or not spam) |
| Sentiment Analysis | Understanding emotions in text (positive, negative, neutral) |
| Machine Translation | Translating text from one language to another (e.g., English to Hindi) |
| Speech Recognition | Converting spoken language into text (used in voice typing) |
| Chatbot Interaction | Understanding and responding to human queries |
E. Key Steps in NLP Pipeline
1. Text Preprocessing
Before analysis, text must be cleaned.
- Tokenization: Breaking sentences into words Example: “I love AI” → [“I”, “love”, “AI”]
- Stop Words Removal: Removing common words like “is”, “the”, “and” Keeps only useful words
- Stemming: Reducing words to their root form “Running”, “Runs” → “Run”
2. Feature Extraction
Converting text into numbers that machines can understand.
Example:
- Using Bag of Words or TF-IDF to represent word frequency in numerical form.
3. Model Training and Prediction
Using the cleaned and converted data to train AI models to perform tasks like:
- Classifying emails
- Answering questions
- Translating sentences
F. Tools and Libraries for NLP (Overview Only)
| Library | Purpose |
|---|---|
| NLTK | Natural Language Toolkit – used for preprocessing |
| spaCy | High-performance NLP library |
| TextBlob | Easy-to-use library for beginners |
| Google Speech API | Converts speech to text |
G. Real-Life Examples of NLP Applications
| Application | Task Performed |
|---|---|
| Google Translate | Machine Translation |
| WhatsApp Voice Typing | Speech Recognition |
| Gmail Spam Filter | Text Classification |
| Amazon Chatbot | Conversational AI |
| Twitter Sentiment Tool | Sentiment Analysis |
H. Activity Suggestion for Students
Activity 1: Show a chatbot window or a Google Assistant conversation.
Ask:
- How does it understand your words?
- Can it handle mistakes or unclear language?
Activity 2: Take a short sentence and:
- Tokenize it
- Remove stop words
- Find root words
Example:
Sentence: “I am learning Artificial Intelligence from books.”
- Tokenization: [“I”, “am”, “learning”, “Artificial”, “Intelligence”, “from”, “books”]
- After removing stop words: [“learning”, “Artificial”, “Intelligence”, “books”]
- After stemming: [“learn”, “Artificial”, “Intelligence”, “book”]
I. Challenges in NLP
| Challenge | Description |
|---|---|
| Ambiguity | Words can have multiple meanings |
| Spelling/Grammar Errors | Can confuse machines |
| Different Languages | Same word may have different meanings |
| Tone and Emotion | Hard for machines to fully understand human emotions |
J. Keywords to Remember
| Term | Meaning |
|---|---|
| NLP | Making computers understand human language |
| Tokenization | Breaking sentences into individual words |
| Stop Words | Common words removed before processing |
| Stemming | Reducing words to their base/root form |
| Sentiment Analysis | Finding emotions in text |
| Speech Recognition | Converting voice into text |
K. Summary of the Unit
- NLP helps machines understand and respond to human language.
- It includes tasks like translation, classification, speech recognition, and sentiment analysis.
- Text preprocessing is important to clean and prepare data.
- NLP is used in voice assistants, chatbots, translation tools, and email filters.
- It has challenges, but it continues to improve with better models and data.