## Tags - Part of: - Related: - Includes: - Additional: ## Main resources - - <iframe src="https://en.wikipedia.org/wiki/Natural_language_processing" allow="fullscreen" allowfullscreen="" style="height:100%;width:100%; aspect-ratio: 16 / 5; "></iframe> ## Landscapes - [[Transformer]] - [[GPT4]], [[Gemini]] ## Written by AI (may include factually incorrect information) - Natural Language Processing (NLP) is a field at the intersection of [[Computer science]], [[Artificial Intelligence]], and [[Linguistics]], focused on the interactions between [[Computing|computers]] and human [[Language|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.