IT-802 (B) RGPV B.Tech
Natural Language Processing (NLP)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
1 paper, latest Dec 2024
Syllabus, notes and important questions for IT-802 (B)
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction 3 topics
- Levels of natural language processing
- Sources of ambiguity and computational challenges
- Real‑life NLP applications such as spell checking, grammar checking, information extraction, question answering, and machine translation
Unit 2 Text Processing Foundations 5 topics
- Character encoding standards (e.g., UTF‑8, Unicode)
- Word segmentation techniques
- Sentence segmentation techniques
- Corpus creation and description
- Corpus analysis methods
Unit 3 Morphology and POS Tagging 6 topics
- Inflectional and derivational morphology
- Morphological analysis and generation using finite‑state transducers
- Word‑class (POS) categories
- Part‑of‑speech tagging
- Maximum Entropy models for POS tagging
- Multi‑word expressions
Unit 4 Language Modeling and Shallow Parsing 12 topics
- Language models and their role in NLP
- Simple N‑gram models
- Parameter estimation and smoothing techniques
- Evaluation of language models
- Phrase, clause and sentence structure
- Shallow parsing and chunking
- Conditional Random Fields (CRF) for shallow parsing
- Lexical semantics
- Word Sense Disambiguation (WSD)
- WordNet lexical database
- Thematic role identification
- Semantic role labeling with CRFs
Unit 5 Advanced NLP Applications 6 topics
- Natural language interfaces
- Text summarization techniques
- Sentiment analysis methods
- Machine translation approaches
- Question answering systems
- Recent trends in NLP research
Exam insights
Counted from 3 past IT-802 (B) papers.
Marks asked per paper, by unit
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Unit 1 Introduction 7 marks
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Unit 2 Text Processing Foundations 6 marks
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Unit 3 Morphology and POS Tagging 7 marks
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Unit 4 Language Modeling and Shallow Parsing 7 marks
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Unit 5 Advanced NLP Applications 8 marks
Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.
Asked again and again
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Explain language models and their role in Natural Language Processing.
Asked 3 times: 2023
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Discuss tokenization and word segmentation techniques used in NLP.
Asked 2 times: 2024
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What is Word Sense Disambiguation? Describe at least two approaches (e.g., supervised and bootstrapping).
Asked 2 times: 2024