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- W2099052600 abstract "Abstract A significant problem in computational language learning is that of inferring the content of well-formedness constraints from input data. In this article, we approach the constraint induction problem as the gradual adjustment of subsymbolic constraints in a connectionist network. In particular, we develop a multi-layer feed-forward network that learns the constraints that underlie restrictions against homorganic consonants, or ‘OCP-Place constraints’, in Arabic roots. The network is trained using standard learning procedures in connection science with a representative sample of Arabic roots. The trained network is shown to classify actual and novel Arabic roots in ways that are qualitatively parallel to a psycholinguistic study of Arabic. Statistical analysis of network behavior also shows that activations of nodes in the hidden layer correspond well with violations of symbolic well-formedness constraints familiar from generative phonology. In sum, it is shown that at least some constraints operative in phonotactic grammar can be learned from data and do not have to be stipulated in advance of learning." @default.
- W2099052600 created "2016-06-24" @default.
- W2099052600 creator A5037077388 @default.
- W2099052600 creator A5044735762 @default.
- W2099052600 creator A5078121912 @default.
- W2099052600 date "2013-05-01" @default.
- W2099052600 modified "2023-09-30" @default.
- W2099052600 title "Phonological constraint induction in a connectionist network: learning OCP-Place constraints from data" @default.
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- W2099052600 doi "https://doi.org/10.1016/j.langsci.2012.10.002" @default.
- W2099052600 hasPublicationYear "2013" @default.
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