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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 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.
The Piotrowski law is a case of the so-called logistic model (cf. logistic equation). It was shown that it covers also language acquisition processes (cf. language acquisition law). Text block law: Linguistic units (e.g. words, letters, syntactic functions and constructions) show a specific frequency distribution in equally large text blocks.
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 ...
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.
Statistical natural language processing uses stochastic, probabilistic and statistical methods, especially to resolve difficulties that arise because longer sentences are highly ambiguous when processed with realistic grammars, yielding thousands or millions of possible analyses.
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.