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 2 -Self - Management Skills - II
Chapter 4 - Entrepreneurial Skills - II
Part B - Subject Specific Skills Question/Answers
Chapter 1 -AI Project Cycle & Ethics
Chapter 5 - Computer VisionChapter 6 - Natural Language Processing
- Question Answer - 1
- Question Answer - 2
Chapter - Natural Language Processing
#What is Natural Language Processing(NLP)? What are the applications of NLP?
Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables computers to understand, interpret, process, and generate human languages such as English, Hindi, Tamil, etc.
In simple words:
👉 NLP helps computers talk to humans and understand human language.
It combines:
- Computer Science
- Artificial Intelligence
- Linguistics
🔹 Example:
- When you ask Siri / Google Assistant a question
- When Google Translate translates languages
- When emails go to Spam automatically
Applications of NLP
1. Speech Recognition
Converts spoken language into text.
👉 In this application, NLP helps the computer understand human speech and convert audio signals into written words.
The system listens to spoken words, identifies sounds (phonemes), understands word patterns, and then forms correct sentences as text.
This is useful when users:
- Speak instead of typing
- Give voice commands to devices
- Need automatic transcription of speech
✅ Examples:
- Voice typing
- Virtual assistants (Siri, Alexa, Google Assistant)
2. Machine Translation
Converts text from one language to another.
👉 NLP analyzes the grammar, structure, and meaning of the source language and then generates an equivalent sentence in the target language.
It focuses on meaning, not just word-to-word translation.
This helps:
- People communicate across different languages
- Read foreign articles and documents
- Learn new languages
✅ Examples:
- Google Translate
- Microsoft Translator
3. Chatbots and Virtual Assistants
Enables machines to communicate with users in natural language.
👉 NLP allows chatbots to understand user questions, identify the intent, and reply with meaningful responses.
The conversation feels natural because the system processes human-like language.
Used for:
- Customer support
- Online help systems
- Educational tutoring
✅ Examples:
- Customer care chatbots
- Siri, Alexa, Google Assistant
4. Sentiment Analysis
Identifies emotions or opinions in text.
👉 NLP analyzes words, phrases, and context to determine whether the emotion behind the text is positive, negative, or neutral.
It is widely used to understand public opinion.
Useful for:
- Analyzing product reviews
- Understanding customer feedback
- Monitoring social media reactions
✅ Examples:
- Product review analysis
- Social media sentiment tools
5. Text Summarization
Converts long text into a short summary.
👉 NLP selects important information from large documents and presents it in shorter, meaningful form while keeping the main idea intact.
Helps in:
- Reading news quickly
- Summarizing reports
- Studying long documents
✅ Examples:
- News summary apps
- Research paper summaries
6. Spam Detection
Identifies unwanted or harmful messages.
👉 NLP analyzes email or message content to detect spam based on text patterns, keywords, and structure.
Suspicious messages are automatically filtered.
Used for:
- Email security
- Fraud prevention
- Safe communication
✅ Examples:
- Email spam filters
- SMS spam detection
7. Information Retrieval
Finds relevant information from large text data.
👉 NLP understands user queries and searches huge databases to retrieve the most relevant information.
Commonly used in:
- Search engines
- Digital libraries
✅ Examples:
- Google search
- Online knowledge databases
8. Text Classification
Categorizes text into predefined groups.
👉 NLP reads text and assigns it to a particular category based on its content.
Useful for:
- Organizing data
- Automating content management
✅ Examples:
- News classification
- Email sorting
9 Named Entity Recognition (NER)
Identifies names, places, organizations, and dates in text.
👉 NLP extracts key information from text to make data easier to analyze and use.
Helpful in:
- Resume scanning
- Document analysis
✅ Examples:
- Entity extraction from documents
#What are the stages of Natural Language Processing(NLP)?
1. Lexical Analysis
Lexical analysis is the first stage where the system breaks the input text into smaller units called tokens.
These tokens may be:
- Words
- Numbers
- Punctuation marks
Example
Sentence:
“NLP is very useful.”
Tokens:
- NLP
- is
- very
- useful
Other operations done in this stage include:
- Removing unnecessary symbols
- Converting words to lowercase
- Stemming or lemmatization (reducing words to their base form)
Purpose: Prepare raw text for further processing.
 |
| Stages of NLP |
2. Syntactic Analysis (Parsing)
This stage checks the grammar and structure of the sentence.
It determines how words are arranged and whether the sentence follows grammatical rules.
Example
Sentence:
“The dog chased the cat.”
The system identifies:
- Subject: The dog
- Verb: chased
- Object: the cat
A parse tree is often created to represent these relationships.
Purpose: Understand the grammatical structure of the sentence.
3. Semantic Analysis
Semantic analysis focuses on understanding the meaning of words and sentences.
Sometimes a word has multiple meanings, so the system must choose the correct one based on context.
Example
Sentence:
“She went to the bank.”
Possible meanings of bank:
- Financial institution
- River side
Semantic analysis determines the correct meaning from context.
Purpose: Extract the actual meaning of the sentence.
4. Discourse Integration
Discourse integration analyzes how sentences relate to each other in a conversation or paragraph.
Example
“Ravi bought a car. He loves it.”
The system must understand:
- He → Ravi
- it → car
This process is called coreference resolution.
Purpose: Maintain context between sentences.
5. Pragmatic Analysis
Pragmatic analysis identifies the speaker’s intention and real-world meaning.
Example
Sentence:
“Can you pass the salt?”
Literal meaning: asking about ability.
Actual meaning: a request to pass the salt.
Pragmatic analysis considers:
- Speaker intention
- Social context
- Real-world knowledge
Purpose: Understand what the speaker really means.
✅ Complete NLP Processing Flow
Lexical Analysis → Break text into words
Syntactic Analysis → Analyze grammar
Semantic Analysis → Understand meaning
Discourse Integration → Connect sentences
Pragmatic Analysis → Interpret intention
Part A - Employability Skills Notes
Chapter 4 - Entrepreneurial Skills - II
Part B - Subject Specific Skills Notes
Chapter 1 -AI Project Cycle & Ethics
Chapter 5 - Computer Vision
Part A - Employability Skills MCQs
Chapter 2 - Self - Management Skills - II
Chapter 4 - Entrepreneurial Skills - II
Part B - Subject Specific Skills MCQs
Chapter 1 -AI Project Cycle & Ethics
Chapter 5 - Computer Vision
Class 10 Resource Corner
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- Class 10 AI Sample Paper 2022-23
- Class 10 AI Sample Paper 2025-26
- Class 10 English --------------------------------------------------
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