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Specifically, readers fixate their eyes on a word for a shorter time when the word occurs in a moderately or highly constraining context, compared to the same word in an unconstrained context. This is true regardless of the word's frequency or length. Readers are also more likely to skip over a word in a highly constraining context only. [5]
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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]
A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. [1] It is a type of linear classifier , i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector .
Reading is the process of taking in the sense or meaning of symbols, often specifically those of a written language, by means of sight or touch. [1] [2] [3] [4]For educators and researchers, reading is a multifaceted process involving such areas as word recognition, orthography (spelling), alphabetics, phonics, phonemic awareness, vocabulary, comprehension, fluency, and motivation.
A dictionary-based predictive system is based on hope that the desired word is in the dictionary. That hope may be misplaced if the word differs in any way from common usage—in particular, if the word is not spelled or typed correctly, is slang, or is a proper noun. In these cases, some other mechanism must be used to enter the word.
A 2004 non-peer-reviewed literature review criticized most of the main instruments used to identify an individual's learning style. [1] In conducting the review, Frank Coffield and his colleagues selected 13 of the most influential models of the 71 models they identified, [1]: 8–9 including most of the models described in this article. They ...
Autocomplete, or word completion, is a feature in which an application predicts the rest of a word a user is typing. In Android and iOS [ 1 ] smartphones , this is called predictive text . In graphical user interfaces , users can typically press the tab key to accept a suggestion or the down arrow key to accept one of several.