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Showing 1 to 15 of 24 results Save | Export
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Cheng-Yu Hsieh; Marco Marelli; Kathleen Rastle – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2024
Most printed Chinese words are compounds built from the combination of meaningful characters. Yet, there is a poor understanding of how individual characters contribute to the recognition of compounds. Using a megastudy of Chinese word recognition (Tse et al., 2017), we examined how the lexical decision of existing and novel Chinese compounds was…
Descriptors: Semantics, Orthographic Symbols, Chinese, Reading Processes
Kaiying Lin – ProQuest LLC, 2024
The field of Linguistics has long been interested in the verb meanings of intransitive verbs and their argument structure, specifically the breakdown of intransitive verbs into unaccusative and unergative verb types. Despite extensive research, a universally applicable explanation for this breakdown remains elusive due in part to the variability…
Descriptors: Mandarin Chinese, Second Language Learning, Second Language Instruction, Semantics
Megan Gotowski – ProQuest LLC, 2022
How do children learn the meaning of words like "pretty" and "tall," which are not only gradable and context dependent (Kennedy & McNally 2005), but encode speaker subjectivity? Despite their complex semantics (Stephenson 2007; Lasersohn 2009; Bylinina 2014), these and other adjectives like them, are some of the most…
Descriptors: Linguistic Theory, Semantics, Language Acquisition, Language Processing
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Johns, Brendan T.; Mewhort, Douglas J. K.; Jones, Michael N. – Cognitive Science, 2019
Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…
Descriptors: Semantics, Learning Processes, Models, Prediction
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McClelland, James L. – First Language, 2020
Humans are sensitive to the properties of individual items, and exemplar models are useful for capturing this sensitivity. I am a proponent of an extension of exemplar-based architectures that I briefly describe. However, exemplar models are very shallow architectures in which it is necessary to stipulate a set of primitive elements that make up…
Descriptors: Models, Language Processing, Artificial Intelligence, Language Usage
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Jaafar, Eman Adil – Arab World English Journal, 2022
This study aims at shedding light on the linguistic significance of collocation networks in the academic writing context. Following Firth's principle "You shall know a word by the company it keeps." The study intends to examine three selected nodes (i.e. research, study, and paper) shared collocations in an academic context. This is…
Descriptors: Academic Language, Computational Linguistics, Computer Software, Periodicals
Nicula, Bogdan; Dascalu, Mihai; Newton, Natalie N.; Orcutt, Ellen; McNamara, Danielle S. – Grantee Submission, 2021
Learning to paraphrase supports both writing ability and reading comprehension, particularly for less skilled learners. As such, educational tools that integrate automated evaluations of paraphrases can be used to provide timely feedback to enhance learner paraphrasing skills more efficiently and effectively. Paraphrase identification is a popular…
Descriptors: Computational Linguistics, Feedback (Response), Classification, Learning Processes
Charlotte Moore – ProQuest LLC, 2021
When learning a language, typically-developing infants face the daunting task of learning both the sounds and the meanings of words. In this dissertation, we focus on a source of variability that complicates the one-to-one relationship between words and their meanings: wordform variability. In Chapter 1 we make a distinction between the micro…
Descriptors: Computational Linguistics, Infants, Language Acquisition, Language Variation
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Eun Seon Chung – Language Learning & Technology, 2024
While previous investigations on online machine translation (MT) in language learning have analyzed how second language (L2) learners use and post-edit MT output, no study as of yet has investigated how the learners process MT errors and what factors affect this process using response and reading times. The present study thus investigates L2…
Descriptors: English (Second Language), Korean, Language Processing, Translation
Maria-Dorinela Dascalu; Stefan Ruseti; Mihai Dascalu; Danielle S. McNamara; Stefan Trausan-Matu – Grantee Submission, 2022
The use of technology as a facilitator in learning environments has become increasingly prevalent with the global pandemic caused by COVID-19. As such, computer-supported collaborative learning (CSCL) gains a wider adoption in contrast to traditional learning methods. At the same time, the need for automated tools capable of assessing and…
Descriptors: Computational Linguistics, Longitudinal Studies, Technology Uses in Education, Teaching Methods
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Song, Yu; Lei, Shunwei; Hao, Tianyong; Lan, Zixin; Ding, Ying – Journal of Educational Computing Research, 2021
Due to benefits for teaching and learning, an increasing number of studies have focused on classroom dialogue and how to make it productive. Coding, in which the transcribed conversation is allocated to a set of features, is commonly employed to deal with the textual data arising from this dialogue. This is generally done manually and cannot…
Descriptors: Semantics, Classification, Classroom Communication, Dialogs (Language)
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Marecka, Marta; McDonald, Alison; Madden, Gillian; Fosker, Tim – International Journal of Bilingual Education and Bilingualism, 2022
Research suggests that second language words are learned faster when they are similar in phonological structure or accent to the words of an individual's first language. Many major theories suggest this happens because of differences in frequency of exposure and context between first and second language words. Here, we examine the independent…
Descriptors: Pictorial Stimuli, Task Analysis, Phonology, Second Language Learning
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Stevens, Jon Scott; Gleitman, Lila R.; Trueswell, John C.; Yang, Charles – Cognitive Science, 2017
We evaluate here the performance of four models of cross-situational word learning: two global models, which extract and retain multiple referential alternatives from each word occurrence; and two local models, which extract just a single referent from each occurrence. One of these local models, dubbed "Pursuit," uses an associative…
Descriptors: Semantics, Associative Learning, Probability, Computational Linguistics
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Ouyang, Long; Boroditsky, Lera; Frank, Michael C. – Cognitive Science, 2017
Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of…
Descriptors: Semiotics, Computational Linguistics, Syntax, Semantics
Peter Organisciak; Michele Newman; David Eby; Selcuk Acar; Denis Dumas – Grantee Submission, 2023
Purpose: Most educational assessments tend to be constructed in a close-ended format, which is easier to score consistently and more affordable. However, recent work has leveraged computation text methods from the information sciences to make open-ended measurement more effective and reliable for older students. This study asks whether such text…
Descriptors: Learning Analytics, Child Language, Semantics, Age Differences
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