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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]
One theory of language acquisition is the comprehensible output hypothesis. Developed by Merrill Swain , the comprehensible output ( CO ) hypothesis states that learning takes place when learners encounter a gap in their linguistic knowledge of the second language (L2).
The most common way to evaluate summaries is ROUGE (Recall-Oriented Understudy for Gisting Evaluation). It is very common for summarization and translation systems in NIST's Document Understanding Conferences. ROUGE is a recall-based measure of how well a summary covers the content of human-generated summaries known as references.
Michael Long first developed the interaction hypothesis in his 1981 work titled "Input, interaction, and second-language acquisition". [7] In this paper, based on indirect evidence, he proposes that modified input and modified interaction when combined facilitate second language acquisition more efficiently than other alternatives (e.g ...
Second-language acquisition is defined as the learning and adopting of a language that is not the learner's native language. Studies [vague] have shown that extroverts acquire a second language better than introverts. [citation needed] One particular study done by Naiman [vague] reflected this point. The subjects were 72 Canadian high school ...
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 use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis : an example of such a ...