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Bootstrapping is a term used in language acquisition in the field of linguistics. It refers to the idea that humans are born innately equipped with a mental faculty that forms the basis of language. It refers to the idea that humans are born innately equipped with a mental faculty that forms the basis of language.
Finally, syntactic bootstrapping proposes that word meanings are acquired through knowledge of a language's syntactic structure. [5] However, regardless of the method of acquisitions, there is a consensus among bootstrappers that bootstrapping theories of lexical acquisition depend on the natural link between semantic meaning and syntactic ...
In general, bootstrapping usually refers to a self-starting process that is supposed to continue or grow without external input. Many analytical techniques are often called bootstrap methods in reference to their self-starting or self-supporting implementation, such as bootstrapping (statistics), bootstrapping (finance), or bootstrapping (linguistics).
A bootstrap paradox, also known as an information loop, an information paradox, [6] an ontological paradox, [7] or a "predestination paradox" is a paradox of time travel that occurs when any event, such as an action, information, an object, or a person, ultimately causes itself, as a consequence of either retrocausality or time travel.
Engelbart's law is the observation that the intrinsic rate of human performance is exponential. [further explanation needed] The law is named after Douglas Engelbart, whose work in augmenting human performance was explicitly based on the realization that although we use technology, the ability to improve on improvements (bootstrapping, "getting better at getting better") resides entirely ...
Semantic bootstrapping is a linguistic theory of child language acquisition which proposes that children can acquire the syntax of a language by first learning and recognizing semantic elements and building upon, or bootstrapping from, that knowledge. [1]
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The studentized bootstrap, also called bootstrap-t, is computed analogously to the standard confidence interval, but replaces the quantiles from the normal or student approximation by the quantiles from the bootstrap distribution of the Student's t-test (see Davison and Hinkley 1997, equ. 5.7 p. 194 and Efron and Tibshirani 1993 equ 12.22, p. 160):