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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.
Statistical learning is the ability for humans and other animals to extract statistical regularities from the world around them to learn about the environment. Although statistical learning is now thought to be a generalized learning mechanism, the phenomenon was first identified in human infant language acquisition.
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.
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 ...
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]
Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. [2] Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target
Elissa Lee Newport is a professor of neurology and director of the Center for Brain Plasticity and Recovery at Georgetown University.She specializes in language acquisition and developmental psycholinguistics, focusing on the relationship between language development and language structure, and most recently on the effects of pediatric stroke on the organization and recovery of language.