Class 10 AI Chapter - NLP Topic - Question/Answer - Arvindzeclass

Class 10 Artificial Intelligent Code 417 Solutions

Session 2025-26

Artificial Intelligence code 417 syllabus pdf class 10 solutions. Class 10 AI Book. Part - A, Part - B, and Python. This article provides complete solution for class 10 AI (Artificial Intelligence) Code - 417 solution according to new syllabus 2025 – 26. In this article first of all go through the AI Code - 417 syllabus and then follow chapter wise notes. Class 10 AI MCQs Quiz with answers.

Part A - Employability Skills Question/Answers

Chapter 1 -Communication Skills - II

Chapter 2 -Self - Management Skills - II

Chapter 3 - ICT Skills - II

Chapter 4 - Entrepreneurial Skills - II

Chapter 5 - Green Skills - II

Part B - Subject Specific Skills Question/Answers

Chapter 1 -AI Project Cycle & Ethics

Chapter 2 - Modelling in AI

Chapter 3 - Evaluating Models

Chapter 4 - Statistical Data

Chapter 5 - Computer Vision

Chapter 6 - Natural Language Processing

Chapter 7 - Advance Python

Chapter - Natural Language Processing

#What is Chatbot? What are the types of Chatbot?

A chatbot is a computer program that can simulate conversation with humans using text or voice. It uses technologies from Artificial Intelligence (AI) and Natural Language Processing (NLP) to understand user questions and provide responses automatically.

Chatbots are commonly used in:

Customer support
Websites and mobile apps
Virtual assistants
Online shopping help
Education platforms

Examples of chatbots include virtual assistants like ChatGPT, Siri, and Google Assistant.

Types of Chatbots

There are several types of chatbots based on how they work.

1. Script Chatbots

Script Chatbot or Rule-based chatbots follow predefined rules and decision trees.

They respond only to specific commands or keywords.
If the user asks something outside the rules, the bot cannot answer.

Example:
User: “What are your business hours?”
Bot: “Our office is open from 9 AM to 6 PM.”

Characteristics

Simple to build
No learning capability
Limited responses

Used in:

Basic customer service
FAQ bots

2. Smart Chatbots

Smart Chatbot or AI chatbots use Artificial Intelligence and NLP to understand user language.

They can learn from data and conversations.
They understand intent and context better.

Example systems include ChatGPT and IBM Watson Assistant.

Characteristics

Understand natural language
Learn from interactions
More flexible and intelligent

Used in:

Smart assistants
Customer support automation
Personal assistants

3. Hybrid Chatbots

Hybrid chatbots combine rule-based systems with AI capabilities.

How they work:

Start with rule-based responses
Use AI for complex queries

Advantages

More accurate responses
Faster development than pure AI bots
Better control over conversations

4. Voice Chatbots

Voice chatbots interact with users through speech instead of text.

Examples:

Amazon Alexa
Google Assistant
Siri

These systems use:

Speech recognition
NLP
Speech synthesis

✅ Simple Definition

A chatbot is a software application that communicates with humans automatically through text or voice.

#What are the differences between script-bot and smart-bot?

The terms script-bot and smart-bot are often used informally to describe two major chatbot approaches.

A script-bot refers to a rule-based chatbot, while a smart-bot refers to an AI-based chatbot that uses Artificial Intelligence and Natural Language Processing to understand human language.

Below are the main differences.

Differences Between Script-Bot and Smart-Bot

Feature	Script-Bot	Smart-Bot
Working Method	Works using predefined scripts, rules, and keywords	Uses AI and NLP to understand user intent
Learning Ability	Cannot learn from conversations	Can learn and improve from data
Flexibility	Limited responses	Can handle many types of questions
Understanding Language	Understands only exact commands or keywords	Understands natural human language
Complex Queries	Cannot answer complex or unexpected questions	Can respond to complex questions
Development	Easier and cheaper to build	More complex and requires training data
Examples	Basic FAQ bots on websites	Systems like ChatGPT, Google Assistant

Example

Script-Bot

User: “What is your email?”
Bot: “Our email is support@example.com.”

If the user asks:
“Can I contact you by email?”
The bot might not understand because the exact keyword is missing.

Smart-Bot

User: “How can I contact your company?”
Bot: “You can contact us by email, phone, or live chat.”

It understands the meaning and intent, not just keywords.

#What is Text Processing? What are the steps involved in it?

Text processing is the method of cleaning, organizing, and transforming raw text into a structured format so that computers can understand and analyze it.
It is a key part of Natural Language Processing.

👉 In simple terms:
Text processing prepares text data for machine understanding.

Text Processing

Steps of Text Processing

Below are the important steps you mentioned, explained clearly:

1. Sentence Segmentation

This step divides a large paragraph into separate sentences.

Example:

“NLP is interesting. It is useful.”

After segmentation:

NLP is interesting.
It is useful.

👉 Purpose: Makes text easier to analyze sentence by sentence.

2. Tokenization

Tokenization breaks sentences into small units called tokens (words).

Example:

“NLP is powerful”

Tokens:

NLP
is
powerful

👉 Purpose: Helps analyze each word separately.

3. Removing Stop Words

Stop words are common words that do not add much meaning.

Examples:

is, the, and, in, on

Example:

“This is a book”

After removing stop words:

book

👉 Purpose: Focus only on important words.

4. Converting Text to Common Case

This step converts all text into the same case (usually lowercase).

Example:

“NLP IS FUN”

After conversion:

“nlp is fun”

👉 Purpose: Avoid treating “NLP” and “nlp” as different words.

5. Stemming

Stemming reduces words to their root/base form by cutting endings.

Example:

playing → play
running → run
studies → studi (may not be a real word)

👉 Purpose: Simplifies words for analysis (fast but less accurate).

6. Lemmatization

Lemmatization converts words into their correct base (dictionary) form.

Example:

running → run
better → good

👉 Purpose: More accurate than stemming because it considers grammar.

#What is the difference between Stemming and Lemmatization in NLP?

xxxBoth stemming and lemmatization are techniques used in Natural Language Processing to reduce words to their base form.
However, they work in very different ways.

1. Stemming

Stemming is a process of cutting off prefixes or suffixes from a word to get its root form.

Key Idea

👉 Uses simple rules (no understanding of meaning or grammar)

Examples

playing → play
running → run
studies → studi ❌ (not a real word)

Characteristics

Fast and simple
May produce incorrect or non-dictionary words
Does not consider context or grammar

2. Lemmatization

Lemmatization is the process of converting a word into its correct base (dictionary) form, called a lemma.

Key Idea

👉 Uses vocabulary + grammar rules (understands meaning)

Examples

running → run
better → good ✅
studies → study

Characteristics

More accurate
Produces meaningful words
Considers context and part of speech
Slower than stemming

Direct Comparison

Feature	Stemming	Lemmatization
Method	Cuts word endings	Uses dictionary & grammar
Output	May be incorrect	Always meaningful
Speed	Fast	Slower
Accuracy	Low	High
Example	studies → studi	studies → study