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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.
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]
A stochastic grammar (statistical grammar) is a grammar framework with a probabilistic notion of grammaticality: Stochastic context-free grammar; Statistical parsing; Data-oriented parsing; Hidden Markov model (or stochastic regular grammar [1]) Estimation theory; The grammar is realized as a language model.
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.
In psycholinguistics, the interaction hypothesis is a theory of second-language acquisition which states that the development of language proficiency is promoted by face-to-face interaction and communication. [1] Its main focus is on the role of input, interaction, and output in second language acquisition. [2]
It is exclusive from attention and understanding, and has been criticized within the field of psychology and second language acquisition. Schmidt and Frota studied noticing in Schmidt as a Portuguese language learner and collected their findings through diary study and audio recordings. The hypothesis was modified in 1994 in light of criticism.
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.
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.