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https://www.um.edu.mt/library/oar/handle/123456789/135151| Title: | “All mimsy were the borogoves” – a discriminative learning model of morphological knowledge in pseudoword inflection |
| Authors: | Nieder, Jessica van de Vijver, Ruben Tomaschek, Fabian |
| Keywords: | Maltese language -- Morphology Grammar, Comparative and general -- Morphology Psycholinguistics Neural networks (Computer science) Language acquisition Computational linguistics Maltese language -- Lexicology |
| Issue Date: | 2023 |
| Publisher: | Routledge |
| Citation: | Nieder, J., van de Vijver, R., & Tomaschek, F. (2023). “All mimsy were the borogoves”–a discriminative learning model of morphological knowledge in pseudo-word inflection. Language, Cognition and Neuroscience, 38(5), 693-710. |
| Abstract: | Grammatical knowledge has often been investigated in wug tests, in which participants inflect pseudo-words. It was argued that in inflecting these pseudo-words, speakers apply their knowledge of word formation. However, it remains unclear what exactly this knowledge is and how it is learned. According to one theory, the knowledge is best characterised as abstractions that specify how units are combined. Another theory maintains that it is best characterised by memory-based analogy. In both cases the knowledge is learned by association based on positive evidence alone. In this paper, we model the classification of pseudo-words to Maltese plurals using a shallow neural network trained with an error-driven learning algorithm. We demonstrate that the classifications mirror those of Maltese speakers in a wug test. Our results indicate that speakers rely on gradient knowledge of a relation between the phonetics of whole words and plural classes, which is learned in an error-driven way. |
| URI: | https://www.um.edu.mt/library/oar/handle/123456789/135151 |
| Appears in Collections: | Scholarly Works - InsLin |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| All_mimsy_were_the_borogoves_–_a_discriminative_learning_model_of_morphological_knowledge_in_pseudoword_inflection(2023).pdf | 3.17 MB | Adobe PDF | View/Open |
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