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Natural language processing (NLP) is an interdisciplinary subfield of computer science and artificial intelligence.It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics.
Neuro-linguistic programming (NLP) is a pseudoscientific approach to communication, personal development and psychotherapy, that first appeared in Richard Bandler and John Grinder's 1975 book The Structure of Magic I. NLP asserts that there is a connection between neurological processes, language and acquired behavioral patterns, and that these ...
The methods of neuro-linguistic programming are the specific techniques used to perform and teach neuro-linguistic programming, [1] [2] which teaches that people are only able to directly perceive a small part of the world using their conscious awareness, and that this view of the world is filtered by experience, beliefs, values, assumptions, and biological sensory systems.
BERT (language model) Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1][2] It learned by self-supervised learning to represent text as a sequence of vectors. It had the transformer encoder architecture. It was notable for its dramatic improvement over ...
Machine learningand data mining. Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on external labels provided by humans. In the context of neural networks, self-supervised learning aims to leverage inherent structures or ...
Natural language understanding. Natural language understanding (NLU) or natural language interpretation (NLI) [1] is a subset of natural language processing in artificial intelligence that deals with machine reading comprehension. NLU has been considered an AI-hard problem. [2]
Neuro-symbolic AI is a type of artificial intelligence that integrates neural and symbolic AI architectures to address the weaknesses of each, providing a robust AI capable of reasoning, learning, and cognitive modeling. As argued by Leslie Valiant [1] and others, [2][3] the effective construction of rich computational cognitive models demands ...
Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as a human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data to expected output values. [ 1 ]