Search results
Results from the WOW.Com Content Network
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.
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.
Statistical semantics is a subfield of computational semantics, which is in turn a subfield of computational linguistics and natural language processing. Many of the applications of statistical semantics (listed above) can also be addressed by lexicon -based algorithms, instead of the corpus -based algorithms of statistical semantics.
The idea behind statistical machine translation comes from information theory.A document is translated according to the probability distribution (|) that a string in the target language (for example, English) is the translation of a string in the source language (for example, French).
Furthermore, research suggests that humans can develop extremely high levels of language and literacy proficiency without any language output or production at all. [6] Studies show that acquirers usually acquire small but significant amounts of new vocabulary through single exposure to a new word found in a comprehensible text. [ 7 ] "