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Categorical overinclusions involve using one word within a category to label a closely related referent that falls in the same category. Examples are seen in references to people (e.g. daddy for all men), animals (e.g. dog for horses and other quadrupeds), vehicles (e.g. truck for bus), foods (e.g. apple for oranges), and numerous other categories.
In overregularization, the regular ways of modifying or connecting words are mistakenly applied to words that require irregular modifications or connections. It is a normal effect observed in the language of beginner and intermediate language-learners, whether native-speaker children or foreign-speaker adults.
The learning problem with the least squares loss function and Tikhonov regularization can be solved analytically. Written in matrix form, the optimal w {\displaystyle w} is the one for which the gradient of the loss function with respect to w {\displaystyle w} is 0.
Instead, word learning can be accounted for through general learning mechanisms such as salience, association, and frequency. [18] Children are thought to notice the objects, actions, or events that are most salient in context, and then to associate them with the words that are most frequently used in their presence. [ 18 ]
Salmon developed a five-stage model of e-learning and e-moderating that for some time has had a major influence where online courses and online discussion forums have been used. [13] In her five-stage model, individual access and the ability of students to use the technology are the first steps to involvement and achievement.
Statistical learning (and more broadly, distributional learning) can be accepted as a component of language acquisition by researchers on either side of the "nature and nurture" debate. From the perspective of that debate, an important question is whether statistical learning can, by itself, serve as an alternative to nativist explanations for ...
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