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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.
Statistical learning theory suggests that, when learning language, a learner would use the natural statistical properties of language to deduce its structure, including sound patterns, words, and the beginnings of grammar. [46] That is, language learners are sensitive to how often syllable combinations or words occur in relation to other syllables.
First book that addressed statistical and neural network learning of language. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics – by Daniel Jurafsky and James H. Martin. [21] Introductory book on language technology.
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.
Processability Theory is now a mature theory of grammatical development of learners' interlanguage. It is cognitively founded (hence applicable to any language), formal and explicit (hence empirically testable), and extended, having not only formulated and tested hypotheses about morphology, syntax and discourse-pragmatics, but having also paved the way for further developments at the ...
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The fact that during language acquisition, children are largely only exposed to positive evidence, [8] meaning that the only evidence for what is a correct form is provided, and no evidence for what is not correct, [9] was a limitation for the models at the time because the now available deep learning models were not available in late 1980s. [10]