Natural Language Processing
NLP theory and reference
Syllabus
| Module | Detailed Content | Hrs | CO |
|---|---|---|---|
| 1 | Introduction to Natural Language Processing: Why NLP? Generic NLP system, Stages of NLP, Challenges in NLP, Applications of NLP. | 05 | CO1 |
| 2 | Morphological Analysis: Morphology, Types of morphology, Role of Regular expression and finite automata in morphology, Stemming vs Lemmatization, Porter stemmer algorithm, Language model- Ngram. | 08 | CO2 |
| 3 | Syntactic Analysis: Part-Of-Speech tagging (POS), POS tag ambiguity, Rule based tagging, Stochastic POS tagging, Parsing with CFG, Sequence labelling: Hidden Markov Model (HMM). | 09 | CO3 |
| 4 | Semantic Analysis: Lexical Semantics, Attachment for fragment of English language, Semantic relations among lexemes & their senses, Wordnet, Word Sense Disambiguation- Dictionary based, Machine Learning based approach. | 08 | CO4 |
| 5 | Pragmatics and Discourse: Introduction to Pragmatics and Discourse analysis, reference phenomenon, reference resolution problem, Syntactic & semantic constraints on co reference, Lappin and Leass' Algorithm for Pronoun Resolution. | 08 | CO5 |
| 6 | Application: Machine translation, Information retrieval, Information extraction (Question-Answer System), Summarization, Sentiment Analysis, Named Entity Recognition. | 07 | CO6 |
Note that the following content is not available in the B.Tech mydy LMS yet hence it has been verified and curated from MBA-Tech mydy LMS as both of them have identical syllabus
Theory Notes
IA1 Important Topics (Provided instead of Question Bank)
- What is NLP
- Stages of NLP
- Generic NLP System
- Different ambiguities in NLP
- What is morphology in NLP, Different morphologies
- Stemming and lemmatisation
MSE Important Topics
- Regular expressions in NLP
- N-gram model
- What is CFG in NLP
- Sequence labelling: HMM
- POS tagging in NLP – different approaches
- POS ambiguity
- Applications of NLP
Along with this, refer to IA1 Important Topics.
Module 1
Introduction to NLP
Module 1.1 • Why NLP, Generic NLP system, Stages, Challenges & Applications
Module 2
Morphological Analysis
Module 2.1 • Morphology types, Regular expressions & finite automata
Stemming vs Lemmatization
Module 2.2 • Porter stemmer algorithm, detailed comparison
N-gram Language Models
Module 2.3 • Language modeling with N-grams
Module 3
Part-of-Speech Tagging
Module 3.1 • POS tagging, tag ambiguity, rule-based & stochastic tagging
HMM & Parsing
Module 3.2 • Hidden Markov Models, Parsing with CFG, Sequence labelling
Module 4
Lexical Semantics
Module 4.1 • Semantic relations among lexemes & their senses
Semantic Analysis
Module 4.2 • Attachment for English language fragments, Wordnet
Word Sense Disambiguation
Module 4.3 • Dictionary-based & Machine Learning approaches
Module 5
Syntactic & Semantic Constraints
Module 5.1 • Co-reference constraints, Lappin & Leass' Algorithm
Pragmatics & Discourse
Module 5.2 • Discourse analysis, reference phenomenon & resolution
Module 6
NLP Applications Part 1
Module 6.1 • Machine translation, Information retrieval & extraction
NLP Applications Part 2
Module 6.2 • Summarization, Sentiment Analysis, Named Entity Recognition
Question Banks
IA1-MSE Preparation (Unofficial Notes)
All IA1 & MSE Important Topics • Q&A format • Exam Ready
ESE Preparation (Unofficial Notes)
ESE Important Questions including Previous Year Questions