Search results
Results from the WOW.Com Content Network
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). Phonemic processing includes remembering the word ...
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 word structure, i.e. first-order logic , and their associated sounds.
These types of inferences are also referred to as "bridging inferences." For example, if a reader came across the following sentences together, they would need to have inferred that the sentences are related to one-another if they are to make any sense of the text as a whole: "Mary poured the water on the bonfire. The fire went out."
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
They claimed that the level of processing information was dependent upon the depth at which the information was being processed; mainly, shallow processing and deep processing. According to Craik and Lockhart, the encoding of sensory information would be considered shallow processing, as it is highly automatic and requires very little focus.
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.
It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a ...
Shallow parsing (also chunking or light parsing) is an analysis of a sentence which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (noun groups or phrases, verb groups, etc.).