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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).
Semantic processing is the deepest level of processing and it requires the listener to think about the meaning of the cue. Studies on brain imaging have shown that, when semantic processing occurs, there is increased brain activity in the left prefrontal regions of the brain that does not occur during different kinds of processing. One study ...
Reading comprehension involves two levels of processing, shallow (low-level) processing and deep (high-level) processing. Deep processing involves semantic processing, which happens when we encode the meaning of a word and relate it to similar words. Shallow processing involves structural and phonemic recognition, the processing of sentence and ...
Multiple studies have now shown that deep semantic processing, the most beneficial type of processing, is associated with neural activity in the ventral regions within the left prefrontal cortex. [8] The role of the prefrontal cortex shows that deeper-level encoding is self-generated and strategic process that would not be able to progress ...
High-level processing – At this step, the input is typically a small set of data, for example, a set of points or an image region, which is assumed to contain a specific object. [32] The remaining processing deals with, for example: Verification that the data satisfies model-based and application-specific assumptions.
Semantic parsing maps text to formal meaning representations. This contrasts with semantic role labeling and other forms of shallow semantic processing, which do not aim to produce complete formal meanings. [9] In computer vision, semantic parsing is a process of segmentation for 3D objects. [10] [11] Major levels of linguistic structure
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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.