1️⃣ What is NLP?
Definition
- NLP (Natural Language Processing) =
A branch of Artificial Intelligence (AI) that helps computers understand, interpret, and generate human language (text or speech). - It connects Human Language ↔ Machine Understanding.
Key Idea
- Human language = messy (slang, spelling errors, different styles).
- NLP gives rules + algorithms + models to handle this language.
2️⃣ Why is NLP Important? (Especially for Banking Exams)
- Banks receive huge data in text form:
- Emails, chat messages, complaints, social media posts
- Documents: KYC, loan documents, agreements, application forms
- NLP helps banks to:
- Understand what customer is saying
- Reply automatically (chatbots)
- Detect fraud/risk from text
- Summarise documents
- Do sentiment analysis (happy/angry customer)
3️⃣ Levels of Language in NLP (Very Short)
| Level | Meaning (Simple) | Example idea |
|---|---|---|
| Morphology | Word formation (root + suffix/prefix) | “unhappy” = un + happy |
| Lexical | Meaning of individual words | “bank”, “interest”, “loan” |
| Syntax | Grammar / sentence structure | Subject + Verb + Object |
| Semantics | Meaning of sentence | “Loan approved” vs “Loan rejected” |
| Pragmatics | Meaning with context | “Can you help me?” = request |
| Discourse | Meaning across multiple sentences / full conversation | Full email thread |
👉 Exam tip: Just remember “M-L-S-S-P-D” (Morph, Lexical, Syntax, Semantics, Pragmatics, Discourse).
4️⃣ Basic NLP Pipeline (Steps)
When a computer processes text, typical steps:
- Input – Text or Speech
- Tokenization – Breaking text into words or sentences
- Stop-word Removal – Removing very common words: a, an, the, is, was,…
- Stemming / Lemmatization – Reducing words to root/base form
- Feature Extraction / Representation – Convert words → numbers (vectors)
- Model / Algorithm – Apply ML/AI model
- Output Task – Classification, translation, summary, etc.
5️⃣ Key Concepts & Definitions
5.1 Tokenization
- Splitting text into smaller units (tokens):
- Sentence tokens
- Word tokens
- Example: “I love banking.” → [“I”, “love”, “banking”]
5.2 Stop Words
- Very common words that do not add much meaning for analysis
- Example: is, am, are, a, the, this, that
- Often removed before processing.
5.3 Stemming 🪓
- Cutting words to their root by simple rules (may not be a real word).
- Example:
- “playing”, “played”, “player” → “play” or “pla” (approx).
- Fast but rough.
5.4 Lemmatization 🔍
- Converting words to meaningful dictionary base form.
- Uses grammar + vocabulary.
- Example:
- “better” → “good”
- “running” → “run”
- More accurate but slower than stemming.
5.5 Bag of Words (BoW)
- Represents text by counting how many times each word appears.
- Does not care about order of words.
- Example (very simple):
| Word | Count in sentence “I love bank, bank loves me” |
|---|---|
| I | 1 |
| love/loves | 2 |
| bank | 2 |
| me | 1 |
5.6 TF–IDF (Term Frequency – Inverse Document Frequency)
- Term Frequency (TF) = how many times a word appears in a document.
- IDF = how rare or special the word is across all documents.
- TF-IDF gives higher weight to important, rare words and lower to very common words.
5.7 Word Embeddings (Vector Representations)
- Each word is converted to a numeric vector.
- Words with similar meaning have vectors close to each other.
- Example models: Word2Vec, GloVe, FastText.
- Helps models understand similarity:
- “loan” close to “credit”
- “fraud” close to “scam”
6️⃣ Types of NLP Models (Very High Level)
| Type | Description (Simple) | Examples |
|---|---|---|
| Rule-based | Fixed grammar & IF–THEN rules | Old chatbots |
| Statistical | Based on probabilities, counts | N-grams, HMM |
| Machine Learning | Uses labelled examples for learning | Naive Bayes, SVM |
| Neural / Deep Learning | Uses neural networks, powerful & data-hungry | RNN, LSTM, Transformer |
👉 Modern NLP = mostly Deep Learning (e.g., Transformers, GPT-type models).
7️⃣ Important NLP Tasks (Very Exam-Oriented)
7.1 Text Classification
- Assign a label to text.
- Examples:
- Spam vs Not Spam mails
- Complaint type (ATM, Loan, Netbanking)
7.2 Sentiment Analysis
- Check whether text is Positive / Negative / Neutral.
- Example: Customer feedback: “Service is very slow” → Negative.
7.3 Named Entity Recognition (NER)
- Finding names in text:
- Person (Mr. Sharma)
- Bank (Bank of Baroda)
- Place (Mumbai)
- Date, Amount, Organisation etc.
7.4 POS Tagging (Part-of-Speech Tagging)
- Label each word as noun, verb, adjective, etc.
- Helps understand sentence structure.
7.5 Machine Translation
- Translate text from one language to another.
- Example: English ↔ Hindi translation systems.
7.6 Text Summarization
- Create short summary from long document.
- Useful for:
- Policy documents, circulars, agreements.
7.7 Question Answering / Chatbots
- System answers questions in natural language.
- Example:
- “What is my account balance?”
- “How to block my card?”
7.8 Speech-to-Text and Text-to-Speech
- Speech Recognition – convert speech → text.
- Speech Synthesis – convert text → spoken voice.
- Used in IVR systems, voice assistants, call centers.
8️⃣ NLP in Banking & Finance (VERY IMPORTANT FOR EXAMS)
8.1 Customer Service
- Chatbots in mobile apps & websites:
- Answer FAQs
- Help in balance enquiry, card block, mini statements.
- Reduce load on call centres, available 24×7.
8.2 Complaint & Feedback Handling
- NLP can:
- Read complaints automatically
- Identify category (ATM, Branch behaviour, Loan delay etc.)
- Check sentiment (angry/happy).
- Helps in faster grievance redressal and customer satisfaction.
8.3 Fraud & Risk Detection
- Analyse:
- Email trails
- Transaction narration
- Suspicious messages/keywords (“urgent transfer”, “lottery”, etc.)
- Identify patterns of fraud / phishing / social engineering.
8.4 Document Processing & KYC
- Extract important fields automatically from:
- Application forms
- KYC documents
- Loan agreements, financial statements
- Speeds up loan processing and reduces manual errors.
8.5 Regulatory & Compliance Monitoring
- Analyse large volumes of:
- Circulars, regulations, policy documents
- Summarise main compliance points.
- Helps banks maintain RBI / SEBI / IRDAI compliance.
9️⃣ Challenges / Limitations of NLP
- Ambiguity – Same sentence can have different meanings
- Example: “I saw a man with a telescope.”
- Sarcasm & Irony – “Great service!” may be sarcastic in a complaint.
- Spelling mistakes, Slang, Short forms – esp. in social media.
- Multilingual Text – Code-mixed language (English + Hindi).
- Domain-specific Words – Banking words like “NPA”, “KYC”, “CTS” need special handling.
🔥 MOST IMPORTANT EXAM POINTS (DIRECT Q/A STYLE)
- Full form of NLP – Natural Language Processing.
- NLP is a subfield of Artificial Intelligence (AI).
- Aim: Make computers understand, interpret, and generate human language.
- Tokenization – splitting text into words/sentences.
- Stop words – very common words ignored in processing (the, is, of, etc.).
- Stemming – cutting words to rough root (play, playing → play).
- Lemmatization – converting to dictionary base form (better → good).
- BoW – Bag of Words, counts word frequency.
- TF-IDF – gives higher weight to important rare words.
- NER – Named Entity Recognition (find names of people, places, orgs, etc.).
- Sentiment Analysis – identify opinion: positive/negative/neutral.
- Chatbots – major NLP application in bank customer service.
- NLP is heavily used in complaint analysis, fraud detection, document reading, KYC automation.
🧠 Quick Memory Tricks / Mnemonics
- Meaning of NLP → “NLP = Natural Language to Programs”
- Human Language → Computer Programs.
- Basic Pipeline – “TSSFMO”
- Tokenize
- Stop-words remove
- Stem / Lemmatize
- Feature Extract
- Model
- Output
- Major Tasks – “CLASS-FSM”
- Classification
- Language Translation
- Answering Questions (QA/chatbots)
- Sentiment analysis
- Summarization
- Fraud text analysis
- Speech-to-text / text-to-speech
- Mention (NER – finding names)
- Banking Uses – “3C + D + F”
- Chatbots
- Complaint handling
- Customer sentiment
- Document/KYC processing
- Fraud/risk detection
⏱ ULTRA-SHORT LAST-MINUTE REVISION SHEET (FOR PRINT)
NLP – Natural Language Processing
- Definition – AI technique to let computers understand & generate human language (text/speech).
- Used for: chatbots, translation, summarization, sentiment analysis, fraud detection, document reading.
Key Steps (Pipeline)
- Tokenization → Stop-words removal → Stemming/Lemmatization → Feature representation (BoW, TF-IDF, embeddings) → Model → Output.
Core Terms
- Tokenization – split into words/sentences
- Stop words – common useless words (the, a, of)
- Stemming – cut to rough root
- Lemmatization – proper base word
- BoW – word count representation
- TF-IDF – importance-based word weights
- NER – find names (person, org, place, amount, date)
- Sentiment Analysis – positive/negative/neutral text
Model Types
- Rule-based → Fixed rules
- Statistical → Probabilities (N-grams, HMM)
- ML → NB, SVM, etc.
- Neural → RNN, LSTM, Transformers (modern systems)
Banking Applications (VERY IMPORTANT)
- 24×7 Chatbots & voice bots
- Automatic complaint & feedback classification
- Sentiment tracking, customer satisfaction
- Fraud detection from text patterns
- Document & KYC data extraction
- Regulatory document summarization
Challenges
- Ambiguity, sarcasm, spelling errors
- Multilingual and domain-specific language
MCQ
🟦 CHAPTER 1 – BASICS OF NLP (Q1–Q10)
Q1. NLP stands for:
a) Natural Learning Processing
b) Natural Language Processing
c) Neural Language Programming
d) Non-Linear Processing
Ans: b)
Q2. NLP is a subfield of:
a) Database Management
b) Artificial Intelligence
c) Operating Systems
d) Computer Networks
Ans: b)
Q3. Main goal of NLP is to:
a) Design databases
b) Allow computers to understand and generate human language
c) Speed up CPU
d) Manage file systems
Ans: b)
Q4. “Human Language ↔ Machine Understanding” best describes:
a) DBMS
b) NLP
c) HTML
d) Blockchain
Ans: b)
Q5. Which of the following is NOT human language data?
a) Email text
b) Chat messages
c) Audio of customer call
d) CPU machine code
Ans: d)
Q6. NLP is MOST useful to handle:
a) Only numeric data
b) Unstructured text and speech data
c) Only structured tables
d) Only image files
Ans: b)
Q7. In banks, NLP is mainly useful because:
a) Banks never use text data
b) Most banking data is graphics
c) Banks receive lots of text data like emails, complaints, chat messages
d) RBI made NLP compulsory
Ans: c)
Q8. Which of the following is NOT an example of NLP?
a) Chatbot answering customer questions
b) ATM dispensing cash
c) System summarizing a long policy document
d) Sentiment analysis of customer feedback
Ans: b)
Q9. “Computer understanding customer complaints in English” is an example of:
a) Image Processing
b) Natural Language Processing
c) Real-time OS
d) Compiler Design
Ans: b)
Q10. In exam questions, NLP is usually placed under:
a) Computer Hardware
b) Computer Networking
c) Artificial Intelligence / Computer Awareness / IT
d) Accounting Standards
Ans: c)
🟦 CHAPTER 2 – LEVELS & PIPELINE OF NLP (Q11–Q20)
Q11. Morphology in NLP deals with:
a) Sentence structure
b) Word formation (root, prefix, suffix)
c) Voice recognition
d) Database design
Ans: b)
Q12. Syntax in NLP is related to:
a) Word meaning only
b) Sound of words
c) Grammar / sentence structure
d) Sentiment of text
Ans: c)
Q13. Semantics in NLP focuses on:
a) Physical sound
b) Meaning of words/sentences
c) Screen resolution
d) Memory allocation
Ans: b)
Q14. Pragmatics in NLP mainly deals with:
a) Meaning without context
b) Meaning with context and usage
c) Only spelling correction
d) Only grammar
Ans: b)
Q15. Correct order of “MLSSPD” levels is:
a) Morphology, Lexical, Syntax, Semantics, Pragmatics, Discourse
b) Lexical, Morphology, Discourse, Pragmatics, Semantics, Syntax
c) Syntax, Semantics, Lexical, Morphology, Discourse, Pragmatics
d) Morphology, Semantics, Syntax, Pragmatics, Lexical, Discourse
Ans: a)
Q16. First step in a typical NLP text pipeline is:
a) Stemming
b) Feature extraction
c) Tokenization
d) Model training
Ans: c)
Q17. Tokenization means:
a) Encrypting text
b) Splitting text into words or sentences
c) Removing punctuation
d) Translating language
Ans: b)
Q18. Removing common words like “the, is, a, an” is called:
a) Stemming
b) Stop-word removal
c) POS tagging
d) Parsing
Ans: b)
Q19. Stemming and lemmatization are used mainly to:
a) Convert text to speech
b) Reduce words to their root/base form
c) Increase file size
d) Remove all nouns
Ans: b)
Q20. A simple, common NLP pipeline order is:
a) Tokenization → Stop-word removal → Stemming/Lemmatization → Feature extraction → Model → Output
b) Model → Output → Tokenization
c) Output → Model → Tokenization
d) Stop-word removal → Tokenization → Output
Ans: a)
🟦 CHAPTER 3 – CORE TECHNIQUES & REPRESENTATIONS (Q21–Q30)
Q21. Stop words are:
a) Important keywords to be highlighted
b) Common words often removed to reduce noise
c) Words that contain numbers
d) Only verbs
Ans: b)
Q22. Which is TRUE for Stemming?
a) Uses dictionary and grammar heavily
b) Always outputs a valid dictionary word
c) Fast and rule-based, may produce rough roots
d) Only works for numbers
Ans: c)
Q23. Which is TRUE for Lemmatization?
a) Ignores grammar
b) Gives meaningful base/dictionary word
c) Less accurate than stemming
d) Only for English
Ans: b)
Q24. In a Bag of Words (BoW) model, a document is represented by:
a) Order of words only
b) Number of sentences only
c) Counts of words appearing in the document
d) Pictures and graphs
Ans: c)
Q25. Major drawback of simple Bag of Words is:
a) Cannot count words
b) Ignores word order and context
c) Cannot be used in computers
d) Works only on small text
Ans: b)
Q26. TF-IDF gives higher weight to words that are:
a) Very common in all documents
b) Rare and important in a specific document
c) Only numbers
d) Only stop words
Ans: b)
Q27. “Term Frequency” in TF-IDF refers to:
a) Number of documents containing the term
b) Number of times the term appears in a document
c) Number of languages the term appears in
d) Number of special characters in the term
Ans: b)
Q28. “Inverse Document Frequency” (IDF) measures:
a) How common a term is across documents
b) How rare a term is across documents
c) The length of a document
d) Number of sentences per document
Ans: b)
Q29. Word Embedding represents words as:
a) Images
b) Tables of strings
c) Numeric vectors in multi-dimensional space
d) XML tags only
Ans: c)
Q30. In word embeddings, words with similar meaning:
a) Have very distant vectors
b) Have similar or close vectors
c) Cannot be compared
d) Are always removed
Ans: b)
🟦 CHAPTER 4 – NLP TASKS & MODELS (Q31–Q40)
Q31. Text Classification aims to:
a) Create audio files
b) Assign a label/category to text
c) Compress documents
d) Encrypt data
Ans: b)
Q32. Sentiment Analysis classifies text into:
a) Good handwriting / bad handwriting
b) Positive / negative / neutral opinion
c) High / low numeric values
d) Only language type
Ans: b)
Q33. Named Entity Recognition (NER) is used to:
a) Recognize file types
b) Identify names like person, organization, location, date, amount etc.
c) Identify grammar errors only
d) Translate documents
Ans: b)
Q34. POS Tagging (Part-of-Speech tagging) means:
a) Assigning sentiment to each document
b) Assigning part-of-speech (noun, verb, adjective, etc.) to each word
c) Splitting text into paragraphs
d) Removing stop words
Ans: b)
Q35. Machine Translation in NLP is used for:
a) Converting images to text
b) Translating text from one language to another
c) Converting analog signals to digital
d) Correcting network errors
Ans: b)
Q36. Text Summarization in NLP:
a) Expands short text into long text
b) Creates a shorter version of document keeping key points
c) Removes all important words
d) Deletes half the document randomly
Ans: b)
Q37. A chatbot that replies to customer queries in natural language is doing:
a) Numerical computation only
b) Natural Language Understanding and Generation
c) Data compression
d) File indexing
Ans: b)
Q38. A “rule-based” NLP system mainly depends on:
a) Neural networks
b) Deep learning only
c) Hand-written grammar rules and patterns
d) Hardware interrupts
Ans: c)
Q39. “Statistical NLP” is based mostly on:
a) Probability and frequency of words/sequences
b) Only hand-coded rules
c) Only images
d) CPU architecture
Ans: a)
Q40. Modern state-of-the-art NLP models are typically based on:
a) Deep learning neural networks (e.g., Transformers)
b) Only manual rules
c) Only spreadsheets
d) Magnetic tapes
Ans: a)
🟦 CHAPTER 5 – NLP IN BANKING & CHALLENGES (Q41–Q50)
Q41. Main use of NLP-based chatbots in banks is to:
a) Print passbooks
b) Provide 24×7 automatic customer support
c) Issue demand drafts
d) Reconcile GL accounts
Ans: b)
Q42. Which of these is NOT a typical NLP use in banking?
a) Handling complaints & feedback
b) Loan document summarization
c) Core banking transaction posting
d) Sentiment analysis on customer reviews
Ans: c)
Q43. An NLP system that reads complaints and classifies them as “ATM / Loan / Netbanking” is doing:
a) Spam filtering only
b) Text classification
c) Speech recognition
d) Data backup
Ans: b)
Q44. Using NLP to detect fraud-related keywords in emails is mainly for:
a) Gaming
b) Risk & fraud detection
c) Social media marketing only
d) Improving printing speed
Ans: b)
Q45. Automatically extracting name, address, PAN number from scanned KYC form using NLP/AI is part of:
a) KYC automation and document processing
b) Network routing
c) Power management
d) Audio encoding
Ans: a)
Q46. One key challenge in NLP is “ambiguity”. It means:
a) Every sentence has one clear meaning
b) Words/sentences can have multiple meanings
c) No grammar is required
d) All languages are same
Ans: b)
Q47. Sarcasm is difficult for NLP because:
a) It is always in another language
b) The literal words may be positive but actual meaning negative (or vice versa)
c) It has no words
d) It cannot be typed
Ans: b)
Q48. Multilingual and “code-mixed” text (e.g., English + Hindi in one sentence) is challenging for NLP because:
a) Computers cannot show multiple fonts
b) Language identification and grammar rules become complex
c) It cannot be stored in memory
d) It is illegal
Ans: b)
Q49. Domain-specific words like “NPA”, “KYC”, “CTS” in banking:
a) Are always stop words
b) Need special handling as they have specific meanings in banking domain
c) Have no impact on NLP
d) Are removed by default
Ans: b)
Q50. For exam perspective, NLP in banking is MOST correctly summarized as:
a) Technology to only speed up FD calculations
b) AI-based language technology used for chatbots, complaint handling, sentiment analysis, fraud detection, and document/KYC processing
c) A new type of bank account
d) Only a security protocol
Ans: b)