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Transformer
[[GPT4]], [[Gemini]]

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AI¶

Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics, focused on the interactions between computers and human languages. It encompasses a wide range of techniques and approaches for processing and understanding natural language. Here's a comprehensive list of various branches and topics within NLP:

1. Text and Speech Processing¶

Text Normalization
Tokenization
Stemming and Lemmatization
Part-of-Speech Tagging
Speech Recognition
Speech Synthesis

2. Syntax and Parsing¶

Syntactic Analysis
Dependency Parsing
Constituency Parsing
Grammar Induction

3. Semantic Analysis¶

Word Sense Disambiguation
Semantic Role Labeling
Named Entity Recognition
Coreference Resolution
Semantic Parsing

4. Pragmatics and Discourse¶

Dialogue Systems and Chatbots
Discourse Analysis
Anaphora and Coreference Resolution
Pragmatic Analysis for Intent and Implicature

5. Machine Translation¶

Statistical Machine Translation
Neural Machine Translation
Rule-Based Machine Translation
Post-Editing and Quality Estimation

6. Information Retrieval¶

Search Engines
Document Indexing and Retrieval
Query Expansion
Relevance Feedback

7. Information Extraction¶

Entity Extraction and Linking
Relation Extraction
Event Extraction
Fact Checking and Verification

8. Sentiment Analysis and Opinion Mining¶

Sentiment Classification
Aspect-Based Sentiment Analysis
Emotion Detection
Opinion Summarization

9. Text Generation and Summarization¶

Automatic Text Summarization
Language Generation Models
Narrative Generation
Data-to-Text Generation

10. Question Answering Systems¶

Factoid Question Answering
Open-Domain Question Answering
Reading Comprehension Models
Visual Question Answering

11. Natural Language Understanding¶

Intent Classification
Slot Filling
Contextual Understanding
Conversational AI

12. Natural Language Generation¶

Template-Based Generation
Neural Text Generation
Data-to-Text Systems
Content Creation and Storytelling

13. Topic Modeling and Text Clustering¶

Latent Dirichlet Allocation (LDA)
Non-Negative Matrix Factorization
Text Clustering Techniques

14. Word and Phrase Embeddings¶

Word2Vec
GloVe
FastText
Contextual Embeddings (e.g., BERT, GPT)

15. Deep Learning in NLP¶

Recurrent Neural Networks (RNNs)
Convolutional Neural Networks (CNNs)
Transformer Models
Transfer Learning in NLP

16. Multimodal NLP¶

Image and Video Captioning
Multimodal Sentiment Analysis
Visual-Textual Content Analysis

17. Evaluation Metrics and Techniques¶

BLEU, ROUGE for Machine Translation and Summarization
Precision, Recall, F1-Score for Classification Tasks

18. Computational Sociolinguistics¶

Language Variation and Change
Computational Analysis of Social Media
Digital Humanities

19. NLP for Low-Resource Languages¶

Cross-Lingual and Multilingual NLP
Language Adaptation Techniques
Resource Creation for Under-Resourced Languages

20. Ethical and Societal Aspects of NLP¶

Bias and Fairness in NLP Models
Ethical Considerations in Language Technologies
Privacy and Security in Text and Speech Processing

NLP is a rapidly evolving field with diverse applications, including chatbots, machine translation, sentiment analysis, and information extraction. It continues to be shaped by advancements in machine learning, deep learning, and data availability.

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