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Du Gan; Kanokporn Numtong; Hao Li; Songyu Jiang – Eurasian Journal of Applied Linguistics, 2024
This study applies the Apriori algorithm to analyse patterns, syntactic structures, and thematic clusters in Chinese studies data from various genres. This study aims to identify recurring linguistic elements in order to shed light on the dynamic nature of the Chinese language across different contexts and time periods. The Apriori algorithm is…
Descriptors: Chinese, Applied Linguistics, Algorithms, Computational Linguistics
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Weinstein, Theresa J.; Ceh, Simon Majed; Meinel, Christoph; Benedek, Mathias – Creativity Research Journal, 2022
Evaluating creativity of verbal responses or texts is a challenging task due to psychometric issues associated with subjective ratings and the peculiarities of textual data. We explore an approach to objectively assess the creativity of responses in a sentence generation task to (1) better understand what language-related aspects are valued by…
Descriptors: Creativity, Sentences, Natural Language Processing, Computation
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Batchelder, Eleanor Olds – Cognition, 2002
Details BootLex, a model using distributional cues to build a lexicon and achieving significant segmentation results with English, Japanese, and Spanish; child- and adult-directed speech, and written text; and variations in coding structure. Compares BootLex with three groups of computational models of the infant segmentation process. Discusses…
Descriptors: Algorithms, Cognitive Development, Cues, Infants
Jordan, Michael I. – 1986
Human behavior shows a variety of serially ordered action sequences. This paper presents a theory of serial order which describes how sequences of actions might be learned and performed. In this theory, parallel interactions across time (coarticulation) and parallel interactions across space (dual-task interference) are viewed as two aspects of a…
Descriptors: Algorithms, Epistemology, Language Patterns, Language Processing
Sternberg, Robert J. – 1979
About 25 children in each of grades 3, 5, 7, 9, and 11 were tested in their ability to solve linear syllogisms, such as: John is taller than Mary. Mary is taller than Pete. Who is tallest--John, Mary, or Pete? Response latencies and error rates decreased across grade levels and sessions. Component latencies also generally decreased with increasing…
Descriptors: Abstract Reasoning, Age Differences, Algorithms, Cognitive Development