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The Limited Capacity Model of Motivated Mediated Message Processing or LC4MP is an explanatory theory that assumes humans have a limited capacity for cognitive processing of information, as it associates with mediated message variables; moreover, they (viewers) are actively engaged in processing mediated information [1] Like many mass communication theories, LC4MP is an amalgam that finds its ...
Phonemic processing includes remembering the word by the way it sounds (e.g. the word tall rhymes with fall). Lastly, we have semantic processing in which we encode the meaning of the word with another word that is similar or has similar meaning. Once the word is perceived, the brain allows for a deeper processing.
The interactive model demonstrates an on-line interaction between the structural and lexical and phonetic levels of sentence processing. Each word, as it is heard in the context of normal discourse, is immediately entered into the processing system at all levels of description, and is simultaneously analyzed at all these levels in the light of ...
Basic research on processing fluency has been applied to marketing, [29] to business names, and to finance. For example, psychologists have determined that, during the week following their IPO, stocks perform better when their names are fluent/easy to pronounce and when their ticker symbols are pronounceable (e.g., KAG) vs. unpronounceable (e.g., KGH).
The idea of skip-gram is that the vector of a word should be close to the vector of each of its neighbors. The idea of CBOW is that the vector-sum of a word's neighbors should be close to the vector of the word. In the original publication, "closeness" is measured by softmax, but the framework allows other ways to measure closeness.
The former is based on the use of datasets designed by experts and composed of word pairs with semantic similarity / relatedness degree estimation. The second way is based on the integration of the measures inside specific applications such as information retrieval, recommender systems, natural language processing, etc.
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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 ...