Question 1(a) [3 marks]

What is Word Embedding technique? List out different word embedding techniques.

Answer:

Word Embedding is a technique that converts words into numerical vectors while preserving semantic relationships between words. It represents words as dense vectors in a high-dimensional space where similar words are closer together.

Table: Different Word Embedding Techniques

Technique	Description	Key Feature
TF-IDF	Term Frequency-Inverse Document Frequency	Statistical measure
Bag of Words (BoW)	Frequency-based representation	Simple counting method
Word2Vec	Neural network-based embedding	Captures semantic relationships
GloVe	Global Vectors for word representation	Combines global and local statistics

TF-IDF: Measures word importance in documents
BoW: Creates vocabulary-based vectors
Word2Vec: Uses CBOW and Skip-gram models
GloVe: Pre-trained embeddings with global context

Mnemonic: "TB-WG" (TF-IDF, BoW, Word2Vec, GloVe)

Question 1(b) [4 marks]

Categorize the different types of Artificial Intelligence and demonstrate it with a diagram.

Answer:

AI can be categorized based on capabilities and functionality.

Diagram:

graph TD
    A[Artificial Intelligence] --> B[Based on Capabilities]
    A --> C[Based on Functionality]
    
    B --> D[Narrow AI/Weak AI]
    B --> E[General AI/Strong AI]
    B --> F[Super AI]
    
    C --> G[Reactive Machines]
    C --> H[Limited Memory]
    C --> I[Theory of Mind]
    C --> J[Self-Awareness]

Table: AI Types Comparison

Category	Type	Description	Example
Capabilities	Narrow AI	Task-specific intelligence	Siri, Chess programs
	General AI	Human-level intelligence	Not yet achieved
	Super AI	Beyond human intelligence	Theoretical concept
Functionality	Reactive	No memory, responds to stimuli	Deep Blue
	Limited Memory	Uses past data	Self-driving cars

Mnemonic: "NGS-RLT" (Narrow-General-Super, Reactive-Limited-Theory)

Question 1(c) [7 marks]

Explain NLU and NLG by giving difference.

Answer:

Natural Language Understanding (NLU) and Natural Language Generation (NLG) are two key components of Natural Language Processing.

Table: NLU vs NLG Comparison

Aspect	NLU	NLG
Purpose	Understands human language	Generates human language
Direction	Input processing	Output generation
Function	Interprets meaning	Creates text
Process	Analysis and comprehension	Synthesis and creation
Examples	Intent recognition, sentiment analysis	Chatbot responses, report generation
Challenges	Ambiguity resolution	Natural text generation

Detailed Explanation:

NLU (Natural Language Understanding):
- Converts unstructured text into structured data
- Performs semantic analysis and intent extraction
- Handles ambiguity and context understanding
NLG (Natural Language Generation):
- Converts structured data into natural language
- Creates coherent and contextually appropriate text
- Ensures grammatical correctness and fluency

Mnemonic: "UI-OG" (Understanding Input, Output Generation)

Question 1(c) OR [7 marks]

List out various Industries where Artificial Intelligence is used and explain any two.

Answer:

Table: AI Applications in Industries

Industry	AI Applications	Benefits
Healthcare	Diagnosis, drug discovery	Improved accuracy
Finance	Fraud detection, trading	Risk management
Manufacturing	Quality control, predictive maintenance	Efficiency
Transportation	Autonomous vehicles, route optimization	Safety
Retail	Recommendation systems, inventory	Personalization
Education	Personalized learning, assessment	Adaptive teaching

Detailed Explanation of Two Industries:

1. Healthcare Industry:

Medical Diagnosis: AI analyzes medical images and patient data
Drug Discovery: Accelerates identification of potential medicines
Personalized Treatment: Tailors therapy based on patient genetics
Benefits: Faster diagnosis, reduced errors, improved outcomes

2. Finance Industry:

Fraud Detection: Identifies suspicious transactions in real-time
Algorithmic Trading: Automated trading based on market patterns
Credit Scoring: Assesses loan default risk accurately
Benefits: Enhanced security, faster processing, better risk management

Mnemonic: "HF-MR-TE" (Healthcare-Finance, Manufacturing-Retail-Transportation-Education)

Question 2(a) [3 marks]

Define the term Machine Learning. Draw the classification diagram of Machine Learning.

Answer:

Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed. It uses algorithms to analyze data, identify patterns, and make predictions.

Diagram:

graph TD
    A[Machine Learning] --> B[Supervised Learning]
    A --> C[Unsupervised Learning]
    A --> D[Reinforcement Learning]
    
    B --> E[Classification]
    B --> F[Regression]
    
    C --> G[Clustering]
    C --> H[Association]
    
    D --> I[Model-based]
    D --> J[Model-free]

Supervised: Uses labeled training data
Unsupervised: Finds patterns in unlabeled data
Reinforcement: Learns through rewards and penalties

Mnemonic: "SUR" (Supervised-Unsupervised-Reinforcement)

Question 2(b) [4 marks]

Differentiate Positive reinforcement and Negative reinforcement.

Answer:

Table: Positive vs Negative Reinforcement

Aspect	Positive Reinforcement	Negative Reinforcement
Definition	Adding reward for good behavior	Removing unpleasant stimulus
Action	Gives something pleasant	Takes away something unpleasant
Purpose	Increase desired behavior	Increase desired behavior
Example	Bonus for good performance	Removing alarm after waking up
Effect	Motivation through rewards	Motivation through relief
Agent Response	Seeks to repeat action	Avoids negative consequences

Key Points:

Positive Reinforcement: Strengthens behavior by adding positive stimulus
Negative Reinforcement: Strengthens behavior by removing negative stimulus
Both types: Aim to increase the likelihood of desired behavior
Difference: Method of encouragement (add vs remove)

Mnemonic: "AR-RN" (Add Reward, Remove Negative)

Question 2(c) [7 marks]

Compare Supervised and Unsupervised learning.

Answer:

Table: Supervised vs Unsupervised Learning

Parameter	Supervised Learning	Unsupervised Learning
Data Type	Labeled data (input-output pairs)	Unlabeled data (only inputs)
Learning Goal	Predict outcomes	Find hidden patterns
Feedback	Has correct answers	No correct answers
Algorithms	SVM, Decision Trees, Neural Networks	K-means, Hierarchical clustering
Applications	Classification, Regression	Clustering, Association rules
Accuracy	Can be measured	Difficult to measure
Complexity	Less complex	More complex
Examples	Email spam detection, Price prediction	Customer segmentation, Market basket analysis

Detailed Comparison:

Supervised Learning:
- Requires training data with known outcomes
- Performance can be easily evaluated
- Used for prediction tasks
Unsupervised Learning:
- Works with data without predefined labels
- Discovers hidden structures in data
- Used for exploratory data analysis

Mnemonic: "LP-PF" (Labeled Prediction, Pattern Finding)

Question 2(a) OR [3 marks]

Define: Classification, Regression, and clustering.

Answer:

Table: ML Task Definitions

Task	Definition	Output Type	Example
Classification	Predicts discrete categories/classes	Categorical	Email: Spam/Not Spam
Regression	Predicts continuous numerical values	Numerical	House price prediction
Clustering	Groups similar data points	Groups/Clusters	Customer segmentation

Detailed Definitions:

Classification: Assigns input data to predefined categories based on learned patterns
Regression: Estimates relationships between variables to predict continuous values
Clustering: Discovers natural groupings in data without prior knowledge of groups

Mnemonic: "CRC" (Categories, Real numbers, Clusters)

Question 2(b) OR [4 marks]

Compare Artificial Neural Network and Biological Neural Network.

Answer:

Table: ANN vs Biological Neural Network

Aspect	Artificial Neural Network	Biological Neural Network
Processing	Digital/Binary	Analog
Speed	Fast processing	Slower processing
Learning	Backpropagation algorithm	Synaptic plasticity
Memory	Separate storage	Distributed in connections
Structure	Layered architecture	Complex 3D structure
Fault Tolerance	Low	High
Energy	High power consumption	Low energy consumption
Parallelism	Limited parallel processing	Massive parallel processing

Key Differences:

ANN: Mathematical model inspired by brain
Biological: Actual brain neural networks
Purpose: ANN for computation, Biological for cognition
Adaptability: Biological networks more flexible

Mnemonic: "DSML-CFEP" (Digital-Speed-Memory-Layer vs Complex-Fault-Energy-Parallel)

Question 2(c) OR [7 marks]

List out various applications of supervised, unsupervised and reinforcement learning.

Answer:

Table: Applications of Different Learning Types

Learning Type	Applications	Real-world Examples
Supervised	Email classification, Medical diagnosis, Stock prediction, Credit scoring	Gmail spam filter, X-ray analysis, Trading algorithms
Unsupervised	Customer segmentation, Anomaly detection, Data compression	Market research, Fraud detection, Image compression
Reinforcement	Game playing, Robotics, Autonomous vehicles, Resource allocation	AlphaGo, Robot navigation, Self-driving cars

Detailed Applications:

Supervised Learning:

Classification: Spam detection, sentiment analysis, image recognition
Regression: Price forecasting, weather prediction, sales estimation

Unsupervised Learning:

Clustering: Market segmentation, gene sequencing, recommendation systems
Association: Market basket analysis, web usage patterns

Reinforcement Learning:

Control Systems: Robot control, traffic management
Optimization: Resource scheduling, portfolio management

Mnemonic: "SCR-CRO" (Supervised-Classification-Regression, Unsupervised-Clustering-Association, Reinforcement-Control-Optimization)

Question 3(a) [3 marks]

Explain Single Layer Forward Network with proper diagram.

Answer:

A Single Layer Forward Network (Perceptron) is the simplest neural network with one layer of weights between input and output.

Diagram:

graph LR
    X1[Input X1] --> |W1| S[Σ]
    X2[Input X2] --> |W2| S
    X3[Input X3] --> |W3| S
    B[Bias b] --> S
    S --> A[Activation Function]
    A --> Y[Output Y]

Components:

Inputs: X1, X2, X3 (feature values)
Weights: W1, W2, W3 (connection strengths)
Bias: Additional parameter for threshold adjustment
Summation: Weighted sum of inputs
Activation: Function to produce output

Mathematical Formula: Y = f(Σ(Wi × Xi) + b)

Mnemonic: "IWSA" (Input-Weight-Sum-Activation)

Question 3(b) [4 marks]

Write a short note on Backpropagation.

Answer:

Backpropagation is a supervised learning algorithm used to train neural networks by adjusting weights based on error calculation.

Table: Backpropagation Process

Phase	Description	Action
Forward Pass	Input propagates through network	Calculate output
Error Calculation	Compare output with target	Find error/loss
Backward Pass	Error propagates backward	Update weights
Weight Update	Adjust weights using gradient	Minimize error

Key Features:

Gradient Descent: Uses calculus to find optimal weights
Chain Rule: Calculates error contribution of each weight
Iterative Process: Repeats until convergence
Learning Rate: Controls speed of weight updates

Steps:

Initialize random weights
Forward propagation to get output
Calculate error between actual and predicted
Backward propagation to update weights

Mnemonic: "FCBU" (Forward-Calculate-Backward-Update)

Question 3(c) [7 marks]

Explain the components of architecture of Feed Forward Neuron Network.

Answer:

Feed Forward Neural Network consists of multiple layers where information flows in one direction from input to output.

Diagram:

graph TD
    subgraph Input Layer
    I1[X1]
    I2[X2]
    I3[X3]
    end
    
    subgraph Hidden Layer
    H1[N1]
    H2[N2]
    H3[N3]
    end
    
    subgraph Output Layer
    O1[Y1]
    O2[Y2]
    end
    
    I1 --> H1
    I1 --> H2
    I1 --> H3
    I2 --> H1
    I2 --> H2
    I2 --> H3
    I3 --> H1
    I3 --> H2
    I3 --> H3
    
    H1 --> O1
    H1 --> O2
    H2 --> O1
    H2 --> O2
    H3 --> O1
    H3 --> O2

Components:

1. Input Layer:

Receives raw data
No processing, just distribution
Number of neurons = number of features

2. Hidden Layer(s):

Performs computation and transformation
Contains activation functions
Can have multiple hidden layers

3. Output Layer:

Produces final results
Number of neurons = number of outputs
Uses appropriate activation for task type

4. Weights and Biases:

Weights: Connection strengths between neurons
Biases: Threshold adjustment parameters

5. Activation Functions:

Introduce non-linearity
Common types: ReLU, Sigmoid, Tanh

Mnemonic: "IHO-WA" (Input-Hidden-Output, Weights-Activation)

Question 3(a) OR [3 marks]

Explain Multilayer Feed Forward ANN with diagram.

Answer:

Multilayer Feed Forward ANN contains multiple hidden layers between input and output layers, enabling complex pattern recognition.

Diagram:

graph LR
    subgraph Input
    I1[X1]
    I2[X2]
    end
    
    subgraph Hidden1
    H11[H1]
    H12[H2]
    end
    
    subgraph Hidden2
    H21[H1]
    H22[H2]
    end
    
    subgraph Output
    O1[Y]
    end
    
    I1 --> H11
    I1 --> H12
    I2 --> H11
    I2 --> H12
    
    H11 --> H21
    H11 --> H22
    H12 --> H21
    H12 --> H22
    
    H21 --> O1
    H22 --> O1

Characteristics:

Deep Architecture: Multiple hidden layers
Complex Patterns: Can learn non-linear relationships
Universal Approximator: Can approximate any continuous function

Mnemonic: "MDC" (Multiple layers, Deep learning, Complex patterns)

Question 3(b) OR [4 marks]

Explain 'ReLU is the most commonly used Activation function.'

Answer:

ReLU (Rectified Linear Unit) is widely used due to its simplicity and effectiveness in deep networks.

Table: Why ReLU is Popular

Advantage	Description	Benefit
Computational Efficiency	Simple max(0,x) operation	Fast processing
Gradient Flow	No vanishing gradient for positive values	Better learning
Sparsity	Outputs zero for negative inputs	Efficient representation
Non-linearity	Introduces non-linear behavior	Complex pattern learning

Mathematical Definition: f(x) = max(0, x)

Comparison with Other Functions:

vs Sigmoid: No saturation problem, faster computation
vs Tanh: Simpler calculation, better gradient flow
Limitations: Dead neurons problem for negative inputs

Why Most Common:

Solves vanishing gradient problem
Computationally efficient
Works well in practice
Default choice for hidden layers

Mnemonic: "CGSN" (Computational, Gradient, Sparsity, Non-linear)

Question 3(c) OR [7 marks]

Explain step by step learning process of Artificial Neural Network.

Answer:

ANN Learning Process involves iterative weight adjustment to minimize prediction error.

Table: Step-by-Step Learning Process

Step	Process	Description
1. Initialization	Set random weights	Small random values
2. Forward Propagation	Calculate output	Input → Hidden → Output
3. Error Calculation	Compare with target	Loss function computation
4. Backward Propagation	Calculate gradients	Error → Hidden ← Input
5. Weight Update	Adjust parameters	Gradient descent
6. Iteration	Repeat process	Until convergence

Detailed Steps:

Step 1: Initialize Weights

Assign small random values to all weights and biases
Prevents symmetry breaking problem

Step 2: Forward Propagation

Input data flows through network layers
Each neuron computes weighted sum + activation

Step 3: Calculate Error

Compare network output with desired output
Use loss functions like MSE or Cross-entropy

Step 4: Backward Propagation

Calculate error gradient for each weight
Use chain rule to propagate error backward

Step 5: Update Weights

Adjust weights using gradient descent
New_weight = Old_weight - (learning_rate × gradient)

Step 6: Repeat Process

Continue until error converges or maximum epochs reached
Monitor validation performance to avoid overfitting

Mnemonic: "IFEBWI" (Initialize-Forward-Error-Backward-Weight-Iterate)

Question 4(a) [3 marks]

List out various advantages and disadvantages of Natural Language Processing.

Answer:

Table: NLP Advantages and Disadvantages

Advantages	Disadvantages
Automation of text processing	Ambiguity in human language
24/7 Availability for customer service	Context Understanding challenges
Multilingual Support capabilities	Cultural Nuances difficulty
Scalability for large datasets	High Computational requirements
Consistency in responses	Data Privacy concerns
Cost Reduction in operations	Limited Creativity in responses

Key Points:

Advantages: Efficiency, accessibility, consistency
Disadvantages: Complexity, resource requirements, limitations
Balance: Benefits outweigh challenges in many applications

Mnemonic: "AMS-ACC" (Automation-Multilingual-Scalability vs Ambiguity-Context-Computational)

Question 4(b) [4 marks]

List out preprocessing techniques in NLP and demonstrate any one with a python program.

Answer:

Table: NLP Preprocessing Techniques

Technique	Purpose	Example
Tokenization	Split text into words/sentences	"Hello world" → ["Hello", "world"]
Stop Words Removal	Remove common words	Remove "the", "is", "and"
Stemming	Reduce words to root form	"running" → "run"
Lemmatization	Convert to dictionary form	"better" → "good"
POS Tagging	Identify parts of speech	"run" → verb
Named Entity Recognition	Identify entities	"Apple" → Organization

Python Program - Tokenization:

import nltk
from nltk.tokenize import word_tokenize, sent_tokenize

# Sample text
text = "Natural Language Processing is amazing. It helps computers understand human language."

# Word tokenization
words = word_tokenize(text)
print("Words:", words)

# Sentence tokenization  
sentences = sent_tokenize(text)
print("Sentences:", sentences)

Mnemonic: "TSSL-PN" (Tokenization-Stop-Stemming-Lemmatization, POS-NER)

Question 4(c) [7 marks]

Explain the phases of NLP.

Answer:

NLP Phases represent the systematic approach to process and understand natural language.

Table: NLP Phases

Phase	Description	Process	Example
Lexical Analysis	Tokenization and word identification	Break text into tokens	"I am happy" → ["I", "am", "happy"]
Syntactic Analysis	Grammar and sentence structure	Parse trees, POS tagging	Identify noun, verb, adjective
Semantic Analysis	Meaning extraction	Word sense disambiguation	"Bank" → financial vs river
Discourse Integration	Context across sentences	Resolve pronouns, references	"He" refers to "John"
Pragmatic Analysis	Intent and context understanding	Consider situation/culture	Sarcasm, idioms interpretation

Detailed Explanation:

1. Lexical Analysis:

First phase of NLP pipeline
Converts character stream into tokens
Removes punctuation and special characters

2. Syntactic Analysis:

Analyzes grammatical structure
Creates parse trees
Identifies sentence components

3. Semantic Analysis:

Extracts meaning from text
Handles word ambiguity
Maps words to concepts

4. Discourse Integration:

Analyzes text beyond sentence level
Maintains context across sentences
Resolves references and connections

5. Pragmatic Analysis:

Considers real-world context
Understands speaker's intent
Handles figurative language

Mnemaid Diagram:

graph LR
    A[Raw Text] --> B[Lexical Analysis]
    B --> C[Syntactic Analysis]
    C --> D[Semantic Analysis]
    D --> E[Discourse Integration]
    E --> F[Pragmatic Analysis]
    F --> G[Understanding]

Mnemonic: "LSSDP" (Lexical-Syntactic-Semantic-Discourse-Pragmatic)

Question 4(a) OR [3 marks]

What is Natural Language Processing? List out its applications.

Answer:

Natural Language Processing (NLP) is a branch of AI that enables computers to understand, interpret, and generate human language in a meaningful way.

Table: NLP Applications

Category	Applications	Examples
Communication	Chatbots, Virtual assistants	Siri, Alexa, ChatGPT
Translation	Language translation	Google Translate
Analysis	Sentiment analysis, Text mining	Social media monitoring
Search	Information retrieval	Search engines
Writing	Grammar checking, Auto-complete	Grammarly, predictive text
Business	Document processing, Spam detection	Email filtering

Key Applications:

Machine Translation: Converting text between languages
Speech Recognition: Converting speech to text
Text Summarization: Creating concise summaries
Question Answering: Providing answers to queries

Mnemonic: "CTAS-WB" (Communication-Translation-Analysis-Search, Writing-Business)

Question 4(b) OR [4 marks]

List out the tasks performed with WordNet in NLTK and demonstrate anyone with a python code.

Answer:

Table: WordNet Tasks in NLTK

Task	Description	Purpose
Synsets	Find synonymous words	Word similarity
Definitions	Get word meanings	Understanding context
Examples	Usage examples	Practical application
Hyponyms	Find specific terms	Hierarchical relationships
Hypernyms	Find general terms	Category identification
Antonyms	Find opposite words	Contrast analysis

Python Code - Synsets and Definitions:

from nltk.corpus import wordnet

# Get synsets for word 'good'
synsets = wordnet.synsets('good')
print("Synsets:", synsets)

# Get definition
definition = synsets[0].definition()
print("Definition:", definition)

# Get examples
examples = synsets[0].examples()
print("Examples:", examples)

Mnemonic: "SDEHA" (Synsets-Definitions-Examples-Hyponyms-Antonyms)

Question 4(c) OR [7 marks]

Explain the types of ambiguities in NLP.

Answer:

NLP Ambiguities occur when text can be interpreted in multiple ways, creating challenges for automated understanding.

Table: Types of Ambiguities

Type	Description	Example	Resolution
Lexical	Multiple meanings of single word	"Bank" (financial/river)	Context analysis
Syntactic	Multiple grammatical interpretations	"Flying planes can be dangerous"	Parse trees
Semantic	Multiple meanings at sentence level	"Time flies like an arrow"	Semantic analysis
Pragmatic	Context-dependent interpretation	"Can you pass the salt?"	Situational context
Referential	Unclear pronoun references	"John told Bob he was wrong"	Discourse analysis

Detailed Explanation:

1. Lexical Ambiguity:

Same word, different meanings
Homonyms and polysemes
Example: "Bat" (animal/sports equipment)

2. Syntactic Ambiguity:

Multiple grammatical structures
Different parse trees possible
Example: "I saw a man with a telescope"

3. Semantic Ambiguity:

Sentence-level meaning confusion
Multiple interpretations possible
Example: "Visiting relatives can be boring"

4. Pragmatic Ambiguity:

Context and intent dependent
Cultural and situational factors
Example: Sarcasm and indirect requests

5. Referential Ambiguity:

Unclear references to entities
Pronoun resolution challenges
Example: Multiple possible antecedents

Resolution Strategies:

Context analysis and machine learning
Statistical disambiguation methods
Knowledge bases and ontologies

Mnemonic: "LSSPR" (Lexical-Syntactic-Semantic-Pragmatic-Referential)

Question 5(a) [3 marks]

Explain Bag of Words with example.

Answer:

Bag of Words (BoW) is a text representation method that converts text into numerical vectors based on word frequency, ignoring grammar and word order.

Table: BoW Process

Step	Process	Description
1. Tokenization	Split text into words	Create vocabulary
2. Vocabulary Creation	Unique words collection	Dictionary of terms
3. Vector Creation	Count word frequencies	Numerical representation

Example:

Documents:

Doc1: "I love machine learning"
Doc2: "Machine learning is amazing"

Vocabulary: [I, love, machine, learning, is, amazing]

BoW Vectors:

Doc1: [1, 1, 1, 1, 0, 0]
Doc2: [0, 0, 1, 1, 1, 1]

Characteristics:

Order Independent: Word sequence ignored
Frequency Based: Counts word occurrences
Sparse Representation: Many zero values

Mnemonic: "TVC" (Tokenize-Vocabulary-Count)

Question 5(b) [4 marks]

What is Word2Vec? Explain its steps.

Answer:

Word2Vec is a neural network-based technique that creates dense vector representations of words by learning from their context in large text corpora.

Table: Word2Vec Models

Model	Approach	Prediction
CBOW	Continuous Bag of Words	Context → Target word
Skip-gram	Skip-gram with Negative Sampling	Target word → Context

Steps of Word2Vec:

1. Data Preparation:

Collect large text corpus
Clean and preprocess text
Create training pairs

2. Model Architecture:

Input layer (one-hot encoded words)
Hidden layer (embedding layer)
Output layer (softmax for prediction)

3. Training Process:

CBOW: Predict target word from context
Skip-gram: Predict context from target word
Use backpropagation to update weights

4. Vector Extraction:

Extract weight matrix from hidden layer
Each row represents word embedding
Typically 100-300 dimensions

Benefits:

Captures semantic relationships
Similar words have similar vectors
Supports arithmetic operations (King - Man + Woman = Queen)

Mnemonic: "DMAT" (Data-Model-Architecture-Training)

Question 5(c) [7 marks]

List out applications of NLP and explain any one in detail.

Answer:

Table: NLP Applications

Application	Description	Industry Use
Machine Translation	Language conversion	Global communication
Sentiment Analysis	Opinion mining	Social media monitoring
Chatbots	Conversational AI	Customer service
Text Summarization	Content condensation	News, research
Speech Recognition	Voice to text	Virtual assistants
Information Extraction	Data mining from text	Business intelligence
Question Answering	Automated responses	Search engines
Spam Detection	Email filtering	Cybersecurity

Detailed Explanation: Sentiment Analysis

Sentiment Analysis is the process of determining emotional tone and opinions expressed in text data.

Components:

Text Preprocessing: Cleaning and tokenization
Feature Extraction: TF-IDF, word embeddings
Classification: Positive, negative, neutral
Confidence Scoring: Strength of sentiment

Process Steps:

Data Collection: Gather text from reviews, social media
Preprocessing: Remove noise, normalize text
Feature Engineering: Convert text to numerical features
Model Training: Use ML algorithms for classification
Prediction: Classify new text sentiment
Evaluation: Measure accuracy and performance

Applications:

Brand Monitoring: Track customer opinions
Product Reviews: Analyze customer feedback
Social Media: Monitor public sentiment
Market Research: Understand consumer preferences

Mnemonic: "MSCTSIQ-S" (Machine-Sentiment-Chatbot-Text-Speech-Information-Question-Spam)

Question 5(a) OR [3 marks]

Explain TFIDF with example.

Answer:

TF-IDF (Term Frequency-Inverse Document Frequency) measures word importance in a document relative to a collection of documents.

Formula: TF-IDF = TF(t,d) × IDF(t)

Where:

TF(t,d) = (Number of times term t appears in document d) / (Total terms in document d)
IDF(t) = log(Total documents / Documents containing term t)

Example:

Documents:

Doc1: "machine learning is good"
Doc2: "learning algorithms are good"
Doc3: "machine algorithms work well"

Table: TF-IDF Calculation for "machine"

Document	TF	IDF	TF-IDF
Doc1	1/4 = 0.25	log(3/2) = 0.18	0.25 × 0.18 = 0.045
Doc2	0/4 = 0	log(3/2) = 0.18	0 × 0.18 = 0
Doc3	1/4 = 0.25	log(3/2) = 0.18	0.25 × 0.18 = 0.045

Key Points:

High TF-IDF: Important word in specific document
Low TF-IDF: Common word across documents
Applications: Information retrieval, text mining

Mnemonic: "TI-FD" (Term frequency, Inverse Document frequency)

Question 5(b) OR [4 marks]

Explain about challenges with TFIDF and BOW.

Answer:

Table: Challenges with TF-IDF and BOW

Challenge	TF-IDF	BOW	Impact
Semantic Understanding	Cannot capture meaning	Ignores word relationships	Poor context understanding
Word Order	Position ignored	Sequence lost	Grammar meaning lost
Sparsity	High-dimensional vectors	Many zero values	Memory inefficient
Vocabulary Size	Large feature space	Grows with corpus	Computational complexity
Out-of-Vocabulary	Unknown words ignored	New words not handled	Limited generalization
Polysemy	Multiple meanings	Same treatment for different senses	Ambiguity issues

Detailed Challenges:

1. Lack of Semantic Understanding:

Words treated as independent features
Cannot understand synonyms or related concepts
"Good" and "excellent" treated differently

2. Loss of Word Order:

"Dog bites man" vs "Man bites dog" same representation
Context and grammar information lost
Sentence structure ignored

3. High Dimensionality:

Vector size equals vocabulary size
Sparse matrices with mostly zeros
Storage and computation problems

4. Context Insensitivity:

Same word different contexts treated equally
"Apple" company vs fruit same representation
Polysemy and homonymy issues

Solutions:

Word Embeddings: Word2Vec, GloVe
Contextual Models: BERT, GPT
N-grams: Capture some word order
Dimensionality Reduction: PCA, SVD

Mnemonic: "SSVO-CP" (Semantic-Sequence-Vocabulary-OOV, Context-Polysemy)

Question 5(c) OR [7 marks]

Explain the working of GloVe.

Answer:

GloVe (Global Vectors for Word Representation) combines global statistical information with local context windows to create word embeddings.

Table: GloVe vs Other Methods

Aspect	GloVe	Word2Vec	Traditional Methods
Approach	Global + Local statistics	Local context windows	Frequency-based
Training	Matrix factorization	Neural networks	Counting methods
Efficiency	Fast training	Slower training	Very fast
Performance	High accuracy	Good accuracy	Limited performance

Working Process:

1. Co-occurrence Matrix Construction:

Count word co-occurrences in context windows
Create global statistics matrix
Xij = number of times word j appears in context of word i

2. Ratio Calculation:

Calculate probability ratios
P(k|i) = Xik / Xi (probability of word k given word i)
Focus on meaningful ratios between probabilities

3. Objective Function:

Minimize weighted least squares objective
J = Σ f(Xij)(wi^T wj + bi + bj - log Xij)²
Where f(x) is weighting function

4. Vector Learning:

Use gradient descent to optimize objective
Learn word vectors wi and context vectors wj
Final representation combines both vectors

Key Features:

Global Statistics:

Uses entire corpus information
Captures global word relationships
More stable than local methods

Efficiency:

Trains on co-occurrence statistics
Faster than neural network methods
Scalable to large corpora

Performance:

Performs well on analogy tasks
Captures both semantic and syntactic relationships
Good performance on similarity tasks

Mathematical Foundation:

J = Σ(i,j=1 to V) f(Xij)(wi^T wj + bi + bj - log Xij)²

Where:

V = vocabulary size
Xij = co-occurrence count
wi, wj = word vectors
bi, bj = bias terms
f(x) = weighting function

Advantages:

Combines Benefits: Global statistics + local context
Interpretable: Clear mathematical foundation
Efficient: Faster training than Word2Vec
Effective: Good performance on various tasks

Applications:

Word Similarity: Find related words
Analogy Tasks: King - Man + Woman = Queen
Text Classification: Feature representation
Machine Translation: Cross-lingual mappings

Mnemonic: "CROF-PGAE" (Co-occurrence-Ratio-Objective-Function, Performance-Global-Advantage-Efficiency)