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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 (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 ...
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.
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 ...
The results of the research highlight that language acquisition is a process of learning through statistical means. Moreover, it raises the possibility that infants possess experience-dependent mechanisms that allow for word segmentation and acquisition of other aspects of language. [40]
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]
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.