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Natural Language Processing (NLP) - Coggle Diagram
Natural Language Processing (NLP)
What is Language?
Formal vs. Natural Language
Formal languages: Python, Java, C with defined rules.
Natural languages: Ambiguity and variability in human language.
Definition of NLP
Interactions between computers and human languages
Computational techniques for analyzing and representing naturally occurring texts.
Goal: Achieving human-like language processing.
Required Understanding for NLP
Linguistics
ormal, structural models of language.
Computer Science
Developing internal data representations and processing structures.
Cognitive Psychology
Modeling language use in a psychologically plausible way.
Key Linguistic Concepts
Phonetics and Phonology
Study of speech sounds.
Morphology
Structure and formation of words.
Lexicon
Understanding words, their meanings, and relations.
Syntax
Grammatical structure of sentences.
Semantics
Meaning of sentences.
Discourse Analysis
Analyzing text as a whole for meaning.
Pragmatics
Extra meaning inferred from context.
History of NLP
1950s
Turing test, Georgetown experiment.
1960s
ELIZA (early chatbot).
1970s
Conceptual ontologies.
1980s
Shift from hand-written rules to machine learning due to increased computational power.
Machine Learning in NLP
Inference of rules from large datasets.
Statistical models and probabilistic decisions.
Applications
Efficient searches, improved with more data.
Challenges in NLP
Phonology
Speech recognition, part-of-speech tagging.
Morphology
Segmentation, understanding morphemes.
Lexicon
Dictionary, word sense disambiguation, named entity recognition.
Syntax
Parsing, auto-summary, relationship extraction.
Semantics
Understanding meaning at all levels.
Discourse Analysis
Topic recognition, speech act classification.
Pragmatics
World knowledge, machine translation, natural language generation and understanding.
Progress in NLP
Mostly Solved
Named Entity Recognition (NER), spam detection, optical character recognition (OCR), part-of-speech (POS) tagging.
Good Progress
Sentiment analysis, co-reference resolution, word sense disambiguation, parsing, information extraction, machine translation.
Hard Problems
Question answering, paraphrasing, summarizing, dialogue, natural language understanding and generation.
Mind Map Structure
NLP Introduction
Language and Linguistics
Common Sense Challenges
Language Basics
Formal vs. Natural Language
Ambiguity in Natural Language
NLP Definition and Goals
Computational Techniques
Human-like Language Processing
Required Knowledge
Linguistics
Computer Science
Cognitive Psychology
Linguistic Concepts
Phonetics & Phonology
Morphology
Lexicon
Links
http://conceptnet5.media.mit.edu/
http://web.media.mit.edu/~push/Kurzweil.html