One of the most significant and practical applications of
artificial intelligence is Natural Language Processing (NLP). NLP is a rapidly
developing field as new techniques and toolsets become available and data
becomes more accessible. This course will introduce you to the basics of
natural language processing and how it is used in modern and emerging
technology. In this course, you will learn about the latest neural network
algorithms used to process linguistic information.
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
o POS (PARTS OF SPEECH) Tagging
o Rule-based POS Tagging
o Stochastic POS Tagging
o Transformation-based Tagging
o Hidden Markov Model (HMM) POS Tagging
o Use of HMM for POS Tagging
·
Unit-2
Word sense disambiguation
o Inception and discourse Processing
o Components of Language
o Grammatical Categories
o Spoken Language Syntax
o Word Fragments
o Concept of Coherence
o Discourse structure
o Reference Resolution
o Types of Referring Expressions
·
Unit-3
Inception and discourse
o Word Sense Disambiguation
o Approaches and Methods to Word Sense
Disambiguation (WSD)
o Applications of Word Sense
·
Unit-4
Information Retrieval
o Information Retrieval
o Classical Problem in Information Retrieval
(IR) System
o Information Retrieval (IR) Model
o Types of Information Retrieval (IR) Model
o Design features of Information retrieval (IR)
systems
o The Boolean Model
