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IBM Technology2022-03-21
NLP#NLU#NLG#AI#Artificial Intelligence#Neural Networks#Natural Language Processing#Natural Language Understanding#Natural Language Generation
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💫 Short Summary

Natural Language Processing (NLP) involves understanding and generating human language with deep learning techniques, while Natural Language Generation (NLG) focuses on computers writing human language responses based on data input. NLP tasks include translation and chatbot conversations, with NLU analyzing text and speech for meaning. NLG involves language rules, morphology, and syntax for appropriate responses. Both NLP and NLG have practical applications in healthcare, customer service, and content creation. Understanding word relationships and meanings is crucial in NLP applications.

✨ Highlights
📊 Transcript
Overview of Natural Language Processing (NLP)
NLP involves using deep learning techniques for computers to understand and generate human language.
NLP tasks include language translation, chatbot conversations, named entity recognition, and word pattern identification.
Natural language understanding (NLU) analyzes text and speech for meaning, considering the nuances of human language.
NLU algorithms interpret sentences differently based on context, highlighting the importance of understanding word relationships and meanings in NLP applications.
NLG focuses on enabling computers to write human language text responses based on data input.
NLG applications require consideration of language rules, morphology, lexicons, syntax, and semantics to phrase responses appropriately.
NLG typically consists of three stages: text planning, sentence planning, and realization.
Various machine learning models like hidden Markov chains, recurrent neural networks, and transformers facilitate the NLG process.
NLG, along with NLU, has practical applications in healthcare diagnosis, online customer service, and content creation like lightboard videos.