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Paape, Dario; Avetisyan, Serine; Lago, Sol; Vasishth, Shravan – Cognitive Science, 2021
We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better…
Descriptors: Computational Linguistics, Indo European Languages, Grammar, Bayesian Statistics
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
Levy, Roger; Fedorenko, Evelina; Breen, Mara; Gibson, Edward – Cognition, 2012
In most languages, most of the syntactic dependency relations found in any given sentence are projective: the word-word dependencies in the sentence do not cross each other. Some syntactic dependency relations, however, are non-projective: some of their word-word dependencies cross each other. Non-projective dependencies are both rarer and more…
Descriptors: Reading Comprehension, Sentences, Form Classes (Languages), Language Processing