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Natural language processing (NLP) is a subfield of computer science and especially 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.
NLP makes use of computers, image scanners, microphones, and many types of software programs. Language technology – consists of natural-language processing (NLP) and computational linguistics (CL) on the one hand, and speech technology on the other. It also includes many application oriented aspects of these.
Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. [1] A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natural language programming is not to be mixed up with ...
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text. The largest and most capable LLMs are generative pretrained transformers (GPTs).
The bag-of-words model (BoW) is a model of text which uses an unordered collection (a "bag") of words.It is used in natural language processing and information retrieval (IR).
Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms.
NLP commonly refers to: Natural language processing , a field of computer science and linguistics Neuro-linguistic programming , a pseudoscientific method aimed at modifying human behavior
Knowledge distillation consists of training a smaller network, called the distilled model, on a data set called the transfer set (which is different than the data set used to train the large model) using cross-entropy as the loss function between the output of the distilled model (|) and the output of the large model ^ (|) on the same record ...