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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 13 of 13 1 The Greedy and Recursive Search for Morphological Productivity Belth, Caleb A.; Payne, Sarah R B; Beser, Deniz... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 2 A Grounded Approach to Modeling Generic Knowledge Acquisition Beser, Deniz; Cecil, Joe; Freedman, Marjorie... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 3 Grounding Word Learning Across Situations Gabbard, Ryan; Lichtefeld, Jacob A; Beser, Deniz... In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 4 Falling Through the Gaps: Neural Architectures as Models of Morphological Rule Learning Beser, Deniz In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 5 A Grounded Approach to Modeling Generic Knowledge Acquisition ... 43rd Annual Meeting of the Cognitive Science Society 2021; Beser, Deniz; Cecil, Joe. - : Underline Science Inc., 2021 BASE Show details 6 A Grounded Approach to Modeling Generic Knowledge Acquisition ... Beser, Deniz; Cecil, Joe; Freedman, Marjorie. - : arXiv, 2021 BASE Show details 7 Lemma Activation in Visual Tasks ... Beser, Deniz. - : Open Science Framework, 2021 BASE Show details 8 Lemma Activation in Visual Tasks ... Beser, Deniz. - : Open Science Framework, 2021 BASE Show details 9 ADAM: A Sandbox for Implementing Language Learning ... Gabbard, Ryan; Beser, Deniz; Lichtefeld, Jacob. - : arXiv, 2021 BASE Show details 10 The Greedy and Recursive Search for Morphological Productivity ... 43rd Annual Meeting of the Cognitive Science Society 2021; Belth, Caleb; Beser, Deniz; Kodner, Jordan; Payne, Sarah; Yang, Charles. - : Underline Science Inc., 2021 Abstract: As children acquire the knowledge of their language's morphology, they invariably discover the productive processes that can generalize to new words. Morphological learning is made challenging by the fact that even fully productive rules have exceptions, as in the well-known case of English past tense verbs, which features the -ed rule against the irregular verbs. The Tolerance Principle is a recent proposal that provides a precise threshold of exceptions that a productive rule can withstand. Its empirical application so far, however, requires the researcher to fully specify rules defined over a set of words. We propose a greedy search model that automatically hypothesizes rules and evaluates their productivity over a vocabulary. When the search for broader productivity fails, the model recursively subdivides the vocabulary and continues the search for productivity over narrower rules. Trained on psychologically realistic data from child-directed input, our model displays developmental patterns observed in ... Keyword: Cognitive Linguistics; Cognitive Neuroscience; Cognitive Science; Management; Social Anthropology URL: https://underline.io/lecture/26962-the-greedy-and-recursive-search-for-morphological-productivity https://dx.doi.org/10.48448/1x39-1033 BASE Hide details 11 Grounding Word Learning Across Situations ... 43rd Annual Meeting of the Cognitive Science Society 2021; Beser, Deniz; Cecil, Joe. - : Underline Science Inc., 2021 BASE Show details 12 Grounding Word Learning Across Situations ... 43rd Annual Meeting of the Cognitive Science Society 2021; Beser, Deniz; Cecil, Joe. - : Underline Science Inc., 2021 BASE Show details 13 Local Processes of Homophone Acquisition Beser, Deniz; Caplan, Spencer In: Proceedings of the Society for Computation in Linguistics (2019) BASE Show details

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