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AD-802 (A) · Natural Language Processing/Official Syllabus

Natural Language Processing (AD-802 (A)) - Official Syllabus

1
Unit 1

Introduction: Natural Language Processing(NLP)

  • Natural Language Processing(NLP): Definition and scope
  • Applications in various domains
  • Challenges and limitations
  • NLP tasks in syntax, semantics, and pragmatics
  • Boolean Model
  • Vector model
  • Probabilistic Model
  • Comparison of classical NLP models: Rule-based model
  • Statistical model
  • Information retrieval model
  • Rule-based machine translation model
  • Probabilistic Graphical model
2
Unit 2

Linguistics and Morphology

  • Linguistic essentials: Phonetics and Phonology
  • Morphology: Morphens
  • Syntax
  • Semantics
  • Pragmatics
  • Semiotics
  • Discourse Analysis
  • Psycholinguistics
  • Corpus Linguistics
  • Word Formation Processes
  • Morphological analysis
  • Morphological Finite State Transducers
3
Unit 3

Word Level Analysis

  • Tokenization
  • Part-of-Speech Tagging (POS Tagging)
  • Lemmatization
  • Stemming
  • Named Entity Recognition (NER)
  • Word Sense Disambiguation (WSD)
  • Word Embedding
  • Types of PoS Tagging: Rule-based
  • Stochastic
  • Transformation-based
  • Lexical
  • Hidden Markov model and Maximum Entropy model
  • n-grams
  • Collocations
  • Applications of NER
4
Unit 4

Syntax Analysis

  • Grammatical Formalisms: Context Free Grammars
  • Grammar rules for English
  • Syntactic parsing: Grammar formalisms and treebanks
  • Efficient parsing for context-free grammars (CFGs)
  • Statistical parsing and probabilistic CFGs (PCFGs)
5
Unit 5

Semantic Analysis

  • Requirements for representation
  • First-Order Logic
  • Description Logics
  • Syntax-Driven Semantic analysis
  • Semantic attachments
  • Word Senses
  • Relations between Senses
  • Thematic Roles
  • selectional restrictions
  • WSD using Supervised
  • Dictionary and Thesaurus
  • Applications of WSD

Reference Books

  • Text Analytics with Python
  • Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3
  • Natural Language Processing and Information Retrieval
  • Speech and Language Processing
  • Natural Language Processing with Python
  • Handbook of natural language processing
  • Foundations of Statistical Natural Language Processing
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