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In this paper, we have tried to show the potential of formal models for the constructivist approach to grammatical structure. We presented a domain-general, meronymy-based learning mechanism, viz. Data-Oriented Parsing, and show how it can be used as an unsupervised learning mechanism (U-DOP) to address learnability issues using constructivist principles such as heterogeneity of representation size and redundancy. We further gave an example of how we can incorporate meaning in U-DOP and showed how such a model can in principle learn from noisy data, although it remains to be seen how such a model behaves given naturalistic data. Although the specifics of the presented models may not be viable (perhaps there are problems with the learning mechanism, perhaps there are cognitively unrealistic assumptions), they show at least how we can formalize our understanding of linguistic learners in such a way that we can shed light on known phenomena and perhaps discover new ones.


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