NATURAL LANGUAGE PROCESSING

Description

Natural language processing (NLP) helps computers to connect with humans in their own language. NLP makes it possible for computers to read text, hear speech, interpret it, even to measure sentiments. NLP is a field of Artificial Intelligence that gives the machines the ability to read, understand and derive meaning from human languages.

Syllabus

Beginners

  • Unit-1 : NLP Introduction and Application Areas
    • About NLP
    • Study of Human Language
    • What is Ambiguity
    • NLP phases
    • Applications of Natural Language Processing
    • Machine Translation
    • Speech Recognition
    • Sentiment Analysis
    • Automatic Summarization
    • Spell Checking
  • Unit-2 Word Level Analysis
    • Regular Expressions
    • Characteristics of Regular Sets
    • What is Finite State Automata
    • Regular Expressions Regular Grammars and Finite Automata
    • Types of Finite State Automation
    • Non-deterministic Finite Automation
    • Morphological Parsing
    • Stems
    • Word Order
  • Unit-3 Linguistic Resources
    • Linguistic resources
    • Corpus Representativeness
    • Corpus Balance
    • Corpus Size
    • Applications of TreeBank Corpus
    • Types of Finite State Automation
  • Unit-4 Semantic and Syntactic Analysis
    • Semantic and Syntactic Analysis
    • Meaning Representation
    • Lexical Semantics
    • Syntactic Analysis
    • Concept of Parser
    • Types of Parsing
    • Concept of Derivation
    • Phrase Structure or Constituency Grammar
    • Dependency Grammar
    • Context Free Grammar

Intermediate

  • Unit-5 PoS (Parts of speech) tagging
    • POS (PARTS OF SPEECH) Tagging
    • Rule-based POS Tagging
    • Stochastic POS Tagging
    • Transformation-based Tagging
    • Hidden Markov Model (HMM) POS Tagging
    • Use of HMM for POS Tagging
  • Unit-2 Word sense disambiguation
    • Inception and discourse Processing
    • Components of Language
    • Grammatical Categories
    • Spoken Language Syntax
    • Word Fragments
    • Concept of Coherence
    • Discourse structure
    • Reference Resolution
    • Types of Referring Expressions
  • Unit-3 Inception and discourse
    • Word Sense Disambiguation
    • Approaches and Methods to Word Sense Disambiguation (WSD)
    • Applications of Word Sense
  • Unit-4 Information Retrieval
    • Information Retrieval
    • Classical Problem in Information Retrieval (IR) System
    • Information Retrieval (IR) Model
    • Types of Information Retrieval (IR) Model
    • Design features of Information retrieval (IR) systems
    • The Boolean Model
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