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Natural Language Processing - Coggle Diagram
Natural Language Processing
Language Basics
Ambiguity Example
Time flies like an arrow
Components:
Phonetics: Sound patterns
Phonology: Speech sound patterns
Morphology: Word structure (e.g., prefixes/suffixes)
Lexicon: Dictionary of words
Syntax: Sentence structure
Semantics: Meaning
Discourse: Text-level meaning
Natural vs Formal Languages:
Formal: Python, Java (strict grammar)
Natural: Human language (ambiguous)
Machine Learning in NLP
Types: Supervised, unsupervised
Uses: Traditional models & probabilistic decisions
Advantages: Learn patterns directly from data
History of NLP
1960s:
Development of early AI programs like Eliza and SHRDLU.
1970s-1990s:
Focus on conceptual ontologies and chatterbots.
1950s:
Early attempts at machine translation.
Post 1990s:
Increased use of machine learning techniques in NLP.
Advanced Topics
Word embedding
Parser & Word Puzzles
Common Sense Knowledge
N-grams
Requires reasoning about implicit knowledge
Process & Achievements
Mostly Solved
Named Entity Recognition (NER)
OCR (Optical Character Recognition)
Spam detection
Good Progress:
Sentiment analysis
Co-reference resolution
Parsing & Information Extraction
Machine translation
Core NLP Challenges
Speech recognition
Part-of-speech tagging
Word sense disambiguation
Machine translation
Natural language generation & understanding
What is NLP?
Definition:
Interaction between computers & natural language
Computational techniques to analyze natural text
Fields Involved:
Computer Science
Cognitive Psychology
Linguistics