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The role of statistical learning in language acquisition has been particularly well documented in the area of lexical acquisition. [1] One important contribution to infants' understanding of segmenting words from a continuous stream of speech is their ability to recognize statistical regularities of the speech heard in their environments. [1]
Statistical language acquisition, a branch of developmental psycholinguistics, studies the process by which humans develop the ability to perceive, produce, comprehend, and communicate with natural language in all of its aspects (phonological, syntactic, lexical, morphological, semantic) through the use of general learning mechanisms operating on statistical patterns in the linguistic input.
Her dissertation, The acquisition of wh-questions in early English multi-word speech, [4] was supervised by Julian Pine. This study analyzed the data from twelve 2 to 3 years old children collected over a year and provided detailed evidence for children's early wh-question development from a constructivist perspective.
In the research, it was found that 8 month old infants were able to use simple statistics to identify word boundaries in speech. The results of the research highlight that language acquisition is a process of learning through statistical means.
Proponents of statistical learning believe that it is the basis for higher level learning, and that humans use the statistical information to create a database which allows them to learn higher-order generalizations and concepts. For a child acquiring language, the challenge is to parse out discrete segments from a continuous speech stream.
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 ...
Quantitative linguistics deals with language learning, language change, and application as well as structure of natural languages. QL investigates languages using statistical methods; its most demanding objective is the formulation of language laws and, ultimately, of a general theory of language in the sense of a set of interrelated languages ...
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [ 1 ] [ 2 ] [ 3 ] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
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