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NATURAL
LANGUAGE
PROCESSING
(NLP)
ZAHEEN BANO COURSE – LECTURE
SERIES
HOW HUMAN COMMUNICATE WITH EACH
OTHER
Use natural language
I Use multiple language
English
Urdu
Punjabi
You ; interpreting – understand – reply
I understand
Human behavior become intelligent
COMPUTER SHOULD REPLICATE THE SAME
THING
Copy the same
Computer interact with
Machine
Human
Implement the same human rules
APPLICATIONS OF NLP
• Speech Recognition
• Widely used
• Google Assistant | Siri by Apple
• Valid Reply
• Sentimental Analysis
• Social Media | Tweet Analysis | Positive, Negative and Neutral
• Machine Translations
• Interpreter - Traditional
• Google Translation
• Validity increased
• Chat Bots
• EBay catboats
• Knowledge based reply
NLP MODEL
Speech
Recognition
Natural
Language
Understanding
Natural
Language
Generation
NLU NL
G
NLU : WHAT DO THE USER
SAY?
THEIR INTENT?
MEANING?
Challenges
•Lexical Ambiguity | The Tank was full
of water
•Syntactic Ambiguity | Old men and
women were take to safe place
•Semantic Ambiguity | The Car hit the
cycle while it was moving
•Pragmatic Ambiguity | The Police is
coming
NATURAL LANGUAGE
GENERATION (NLG)
• What should we say to user?
• It should be intelligent and conversational
• Text and Sentence Planning
• Deal with structured data
CORPUS
Natural language Processing.pptx

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Natural language Processing.pptx

  • 2. HOW HUMAN COMMUNICATE WITH EACH OTHER Use natural language I Use multiple language English Urdu Punjabi You ; interpreting – understand – reply I understand Human behavior become intelligent
  • 3. COMPUTER SHOULD REPLICATE THE SAME THING Copy the same Computer interact with Machine Human Implement the same human rules
  • 4. APPLICATIONS OF NLP • Speech Recognition • Widely used • Google Assistant | Siri by Apple • Valid Reply • Sentimental Analysis • Social Media | Tweet Analysis | Positive, Negative and Neutral • Machine Translations • Interpreter - Traditional • Google Translation • Validity increased • Chat Bots • EBay catboats • Knowledge based reply
  • 6. NLU : WHAT DO THE USER SAY? THEIR INTENT? MEANING? Challenges •Lexical Ambiguity | The Tank was full of water •Syntactic Ambiguity | Old men and women were take to safe place •Semantic Ambiguity | The Car hit the cycle while it was moving •Pragmatic Ambiguity | The Police is coming
  • 7. NATURAL LANGUAGE GENERATION (NLG) • What should we say to user? • It should be intelligent and conversational • Text and Sentence Planning • Deal with structured data CORPUS