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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.
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 ...
] NLP proponents also found that pacing and leading the various cues tended to build rapport, and allowed people to communicate more effectively. Certain studies suggest that using similar representational systems to another person can help build rapport [ 15 ] whilst other studies have found that merely mimicking or doing so in isolation is ...
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).
A language model is a model of natural language. [1] Language models are useful for a variety of tasks, including speech recognition, [2] machine translation, [3] natural language generation (generating more human-like text), optical character recognition, route optimization, [4] handwriting recognition, [5] grammar induction, [6] and information retrieval.
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.
In natural language processing, 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]
As of 2020, BERT is a ubiquitous baseline in natural language processing (NLP) experiments. [3] BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. [4]