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Conversely, deep processing (e.g., semantic processing) results in a more durable memory trace. [1] There are three levels of processing in this model. Structural processing, or visual, is when we remember only the physical quality of the word (e.g. how the word is spelled and how letters look).
Natural computing, [1] [2] also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute.
Brain-inspired computing refers to computational models and methods that are mainly based on the mechanism of the brain, rather than completely imitating the brain. The goal is to enable the machine to realize various cognitive abilities and coordination mechanisms of human beings in a brain-inspired manner, and finally achieve or exceed Human ...
Recurrent neural networks (RNNs) are a class of artificial neural network commonly used for sequential data processing. Unlike feedforward neural networks, which process data in a single pass, RNNs process data across multiple time steps, making them well-adapted for modelling and processing text, speech, and time series.
The term "methodology" is associated with a variety of meanings. In its most common usage, it refers either to a method, to the field of inquiry studying methods, or to philosophical discussions of background assumptions involved in these processes.
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Transfer-appropriate processing (TAP) is a type of state-dependent memory specifically showing that memory performance is not only determined by the depth of processing (where associating meaning with information strengthens the memory; see levels-of-processing effect), but by the relationship between how information is initially encoded and how it is later retrieved.
Deep linguistic processing is a natural language processing framework which draws on theoretical and descriptive linguistics. It models language predominantly by way of theoretical syntactic/semantic theory (e.g. CCG , HPSG , LFG , TAG , the Prague School ).