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Natural Language Processing (NLP) - Coggle Diagram
Natural Language Processing (NLP)
What is NLP?
Interaction between computers & human languages
Analyzes and represents natural texts
Goal: Human-like language processing
Disciplines Involved
Linguistics – structure of language
Computer Science – data representation & processing
Cognitive Psychology – language use & human cognition
Key Components
Phonetics & Phonology – speech sounds
Morphology – smallest units of meaning (morphemes)
Lexicon – words, meanings, relationships
Syntax – grammatical structure
Semantics – meaning at sentence level
Discourse Analysis – connections between sentences
Pragmatics – implied meanings (context, intentions)
History
1950s – Turing Test, early translation
1960s – ELIZA (early chatbot)
1970s – Ontologies & logic-based programs
1980s+ – Machine learning replaces hand-written rules
NLP Techniques
Machine Learning – statistical models, learn from data
Ontologies – structured knowledge (Semantic Web)
Common NLP Tasks
Part-of-speech tagging
Named Entity Recognition (NER)
Co-reference resolution
Word sense disambiguation
Parsing & Information Extraction
Machine Translation
Sentiment Analysis
Optical Character Recognition (OCR)
NLP Challenges
Ambiguity – e.g., “He saw her duck”
Common sense knowledge – implicit understanding
World knowledge – facts, context
Dialogue systems – paraphrasing, summarizing, QA
Applications & Examples
Spam filters, Search
Sentiment analysis (public opinion)
Machine translation (Google Translate)
Chatbots & virtual assistants
Progress & Limitations
Mostly solved:
OCR, NER, POS tagging
Good progress:
Sentiment analysis
Co-reference resolution
Word sense disambiguation
Parsing & extraction
Really hard:
True understanding
Common sense reasoning
Common Sense & Reasoning
Logic puzzles (e.g., Farmer, Wolf, Goat)
Complex planning (e.g., surprise gift problem)
ConceptNet & commonsense knowledge bases