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Representational systems (NLP) Representational systems (also abbreviated to VAKOG[1]) is a postulated model from neuro-linguistic programming, [2] a collection of models and methods regarding how the human mind processes and stores information. The central idea of this model is that experience is represented in the mind in sensorial terms, i.e ...
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.
e. In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning. In classical reinforcement learning, an intelligent agent's goal ...
Question answering. Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. [1]
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 ...
Battery-powered systems can be used by guides who are speaking to clients on walking tours. Public address systems consist of input sources (microphones, sound playback devices, etc.), amplifiers, control and monitoring equipment (e.g., LED indicator lights, VU meters, headphones), and loudspeakers.
e. In natural language processing (NLP), a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valued vector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning. [1]
The high-level architecture of IBM's DeepQA used in Watson [9]. Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.