Your roadmap · आपका रोडमैप
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Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
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Part 1 of the syllabusपाठ्यक्रम भाग 1Unit-wise coaching with notes and doubt classes.
Unit 1: Introduction and Language ModellingUnit 2: Word Level AnalysisUnit 3: Syntactic Analysis
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Semantic AnalysisUnit 5: Discourse and NLP Applications
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 7 exam (university / board)विश्वविद्यालय / बोर्ड परीक्षाPrevious papers and full revision before the exam.
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Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
Syllabus · पाठ्यक्रम
1 subject · 5 chapters · 33 topics
01Unit 1: Introduction and Language Modelling
7 topics+
- Origins and challenges of NLP
- Language and grammar
- Processing Indian languages
- NLP applications and information retrieval
- Regular expressions and finite state automata
- Morphology, stemming and tokenization
- Spelling error detection and minimum edit distance
02Unit 2: Word Level Analysis
7 topics+
- Unsmoothed N-grams
- Evaluating N-grams: perplexity
- Smoothing: Laplace, interpolation and backoff
- Word classes and POS tagging
- Rule-based and stochastic (HMM) tagging
- Viterbi algorithm
- Transformation-based (Brill) tagging
03Unit 3: Syntactic Analysis
7 topics+
- Context-free grammars
- Grammar rules for English and treebanks
- Normal forms (CNF)
- Top-down and bottom-up parsing
- CYK parsing
- Probabilistic CFG
- Feature structures and unification
04Unit 4: Semantic Analysis
6 topics+
- Meaning representation and first order logic
- Syntax-driven semantic analysis
- Word senses and relations (synonymy, hyponymy)
- Thematic roles and selectional restrictions
- Word sense disambiguation: supervised, Lesk, bootstrapping
- Word similarity: thesaurus and distributional
05Unit 5: Discourse and NLP Applications
6 topics+
- Discourse cohesion and coherence
- Reference and coreference resolution
- Machine translation
- Question answering and information extraction
- Sentiment analysis and summarization
- TF-IDF and word embeddings
About this course
You will be able to
- Explain the levels of language analysis and challenges of NLP, including Indian languages
- Use regular expressions, finite state automata, stemming and minimum edit distance
- Build and evaluate N-gram language models with smoothing
- Perform POS tagging with rule-based, HMM (Viterbi) and transformation-based methods
- Parse sentences with CFG, CYK and probabilistic CFG
- Explain semantics, word sense disambiguation and NLP applications such as MT, QA and sentiment analysis
Who it is for
- B.Tech Information Technology students of Semester 7 (AKTU)
- Students of other universities studying the same subject who want unit-wise coaching
What you need
- Basic probability and discrete mathematics
- Python programming (NLTK / spaCy useful)
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
Questions
Which syllabus does the Natural Language Processing course follow?
It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Natural Language Processing. If your own university's syllabus differs, the centre follows your university syllabus.
Will this help in the university examination?
Yes. Teaching is unit-wise as per the AKTU scheme, with previous-year question practice, MCQ tests and doubt sessions before the semester exam.
Do we write NLP programs?
Yes. Concepts are shown with small Python programs using NLTK, so you understand N-grams, tagging and parsing practically.
