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Natural Language Processing - Coggle Diagram
Natural Language Processing
Definition and Overview
The field of study focused on the interaction between computers and human (natural) languages.
Importance
Enables computers to understand, interpret, and generate human language.
Applications
Chatbots and Virtual Assistants
Machine Translation
Sentiment Analysis
Information Retrieval
Techniques and Algorithms
Statistical Methods
N-grams
Hidden Markov Models (HMM)
Bayesian Networks
Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning
Recurrent Neural Networks (RNN)
Long Short-Term Memory (LSTM)
Transformer Models (e.g., BERT, GPT)
Rule-Based Approaches
Grammar Rules
Lexicons
NLP Tasks
Text Processing
Tokenization
Stop Words Removal
Normalization
Text Analysis
Named Entity Recognition (NER)
Part-of-Speech (POS) Tagging
Sentiment Analysis
Machine Translation
Statistical Machine Translation (SMT)
Neural Machine Translation (NMT)
Speech Processing
Text-to-Speech (TTS)
Automatic Speech Recognition (ASR)
Question Answering
Retrieval-Based
Generative
Core Concepts
Syntax
Example: Grammar, Parsing
Rules that govern the structure of sentences.
Semantics
Example: Word Sense Disambiguation, Semantic Parsing
Meaning of words and sentences.
Pragmatics
Contextual use of language.
Example: Conversation Analysis, Speech Acts
Morphology
Structure of words.
Example: Stemming, Lemmatization
Phonology
Sound structure of language.
Example: Phonetics, Phonemic Analysis
Data and Resources
Corpora
Example: Penn Treebank, Brown Corpus
Large and structured sets of texts.
Lexicons and Thesauri
WordNet, Roget's Thesaurus
Annotated Datasets
CoNLL, GLUE Benchmark
APIs and Tools
SpaCy, NLTK, OpenNLP, Stanford NLP
Web-Sites
https://www.techtarget.com/searchenterpriseai/definition/natural-language-processing-NLP
https://www.ibm.com/topics/natural-language-processing
https://www.geeksforgeeks.org/natural-language-processing-overview/
Applications and Examples
Chatbots and Virtual Assistants
Siri, Alexa, Google Assistant
Machine Translation
Google Translate, DeepL
Sentiment Analysis
Social Media Monitoring, Customer Feedback
Information Retrieval
Search Engines, Document Summarization
Text Generation
Content Creation, Storytelling
Challenges in NLP
Ambiguity
Lexical Ambiguity
Syntactic Ambiguity
Context Understanding
Dealing with Homonyms and Polysemy
Language Variability
Dialects, Slang, Code-Switching
Resource Scarcity
Low-Resource Languages
Domain-Specific Texts
Ethical Considerations
Bias in NLP Models
Privacy Concerns