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Showing 1 to 15 of 54 results Save | Export
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Daniel Swingley; Robin Algayres – Cognitive Science, 2024
Computational models of infant word-finding typically operate over transcriptions of infant-directed speech corpora. It is now possible to test models of word segmentation on speech materials, rather than transcriptions of speech. We propose that such modeling efforts be conducted over the speech of the experimental stimuli used in studies…
Descriptors: Sentences, Word Recognition, Psycholinguistics, Infants
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Hei-Chia Wang; Yu-Hung Chiang; I-Fan Chen – Education and Information Technologies, 2024
Assessment is viewed as an important means to understand learners' performance in the learning process. A good assessment method is based on high-quality examination questions. However, generating high-quality examination questions manually by teachers is a time-consuming task, and it is not easy for students to obtain question banks. To solve…
Descriptors: Natural Language Processing, Test Construction, Test Items, Models
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Sang-Gu Kang – Journal of Pan-Pacific Association of Applied Linguistics, 2023
Generative AIs such as Google Bard are known to be equipped with techniques and grammatical principles of human language based on a large corpus of text and code that allow them to generate natural-sounding language, and also identify and correct grammatical errors in human-written texts. Still, they are not perfect language generators, and this…
Descriptors: Artificial Intelligence, Natural Language Processing, Error Correction, Writing (Composition)
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Cerstin Mahlow; Malgorzata Anna Ulasik; Don Tuggener – Reading and Writing: An Interdisciplinary Journal, 2024
Producing written texts is a non-linear process: in contrast to speech, writers are free to change already written text at any place at any point in time. Linguistic considerations are likely to play an important role, but so far, no linguistic models of the writing process exist. We present an approach for the analysis of writing processes with a…
Descriptors: Writing Processes, Methods, Sentences, Evaluation Methods
Morrison, Ryan – Online Submission, 2022
Large Language Models (LLM) -- powerful algorithms that can generate and transform text -- are set to disrupt language learning education and text-based assessments as they allow for automation of text that can meet certain outcomes of many traditional assessments such as essays. While there is no way to definitively identify text created by this…
Descriptors: Models, Mathematics, Automation, Natural Language Processing
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Albertson, Brendon – Research-publishing.net, 2021
A Computer-Assisted Language Learning (CALL) application, TextMix, was developed as a proof-of-concept for applying Natural Language Processing (NLP) sentence chunking techniques to creating 'sentence scramble' learning tasks. TextMix addresses limitations of existing applications for creating sentence scrambles by using NLP to parse and scramble…
Descriptors: Computer Assisted Instruction, Second Language Learning, Natural Language Processing, Sentences
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C. H., Dhawaleswar Rao; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2023
Multiple-choice question (MCQ) plays a significant role in educational assessment. Automatic MCQ generation has been an active research area for years, and many systems have been developed for MCQ generation. Still, we could not find any system that generates accurate MCQs from school-level textbook contents that are useful in real examinations.…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Automation, Test Items
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Dasgupta, Ishita; Guo, Demi; Gershman, Samuel J.; Goodman, Noah D. – Cognitive Science, 2020
As modern deep networks become more complex, and get closer to human-like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present…
Descriptors: Natural Language Processing, Man Machine Systems, Heuristics, Sentences
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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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Uzun, Kutay; Ulum, Ömer Gökhan – Acuity: Journal of English Language Pedagogy, Literature and Culture, 2022
This study aimed to utilize sentiment and sentence similarity analyses, two Natural Language Processing techniques, to see if and how well they could predict L2 Writing Performance in integrated and independent task conditions. The data sources were an integrated L2 writing corpus of 185 literary analysis essays and an independent L2 writing…
Descriptors: Natural Language Processing, Second Language Learning, Second Language Instruction, Writing (Composition)
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Švábenský, Valdemar; Baker, Ryan S.; Zambrano, Andrés; Zou, Yishan; Slater, Stefan – International Educational Data Mining Society, 2023
Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and…
Descriptors: Generalization, Computer Mediated Communication, MOOCs, State Universities
Khashabi, Daniel – ProQuest LLC, 2019
"Natural language understanding" (NLU) of text is a fundamental challenge in AI, and it has received significant attention throughout the history of NLP research. This primary goal has been studied under different tasks, such as Question Answering (QA) and Textual Entailment (TE). In this thesis, we investigate the NLU problem through…
Descriptors: Natural Language Processing, Artificial Intelligence, Task Analysis, Questioning Techniques
Chen, Su; Fang, Ying; Shi, Genghu; Sabatini, John; Greenberg, Daphne; Frijters, Jan; Graesser, Arthur C. – Grantee Submission, 2021
This paper describes a new automated disengagement tracking system (DTS) that detects learners' maladaptive behaviors, e.g. mind-wandering and impetuous responding, in an intelligent tutoring system (ITS), called AutoTutor. AutoTutor is a conversation-based intelligent tutoring system designed to help adult literacy learners improve their reading…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Attention, Adult Literacy
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de Sousa Netto, Manoel Camilo; Pinto, Adilson Luiz; Semeler, Alexandre Ribas – Education for Information, 2019
Law enforcement agencies in Brazil communicate with each other using documents formalized in a language predominantly written in pure text. However, postmodern criminal organizations play complex roles, making it difficult to describe their actions using only text. Visual memory is relevant to learning and thus should be applied. Learning about…
Descriptors: Foreign Countries, Visual Learning, Crime, Law Enforcement
Sharp, Rebecca Reynolds – ProQuest LLC, 2017
We address the challenging task of "computational natural language inference," by which we mean bridging two or more natural language texts while also providing an explanation of how they are connected. In the context of question answering (i.e., finding short answers to natural language questions), this inference connects the question…
Descriptors: Computation, Natural Language Processing, Inferences, Questioning Techniques
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