Where Defaults Don't Help: the Case of the German Plural System
<|reference_start|>Where Defaults Don't Help: the Case of the German Plural System: The German plural system has become a focal point for conflicting theories of language, both linguistic and cognitive. We present simulation results with three simple classifiers - an ordinary nearest neighbour algorithm, Nosofsky's `Generalized Context Model' (GCM) and a standard, three-layer backprop network - predicting the plural class from a phonological representation of the singular in German. Though these are absolutely `minimal' models, in terms of architecture and input information, they nevertheless do remarkably well. The nearest neighbour predicts the correct plural class with an accuracy of 72% for a set of 24,640 nouns from the CELEX database. With a subset of 8,598 (non-compound) nouns, the nearest neighbour, the GCM and the network score 71.0%, 75.0% and 83.5%, respectively, on novel items. Furthermore, they outperform a hybrid, `pattern-associator + default rule', model, as proposed by Marcus et al. (1995), on this data set.<|reference_end|>
@article{nakisa1996where,
    title={Where Defaults Don't Help: the Case of the German Plural System},
    author={Ramin Charles Nakisa and Ulrike Hahn (Oxford University)},
    journal={arXiv preprint arXiv:cmp-lg/9605020},
    year={1996},
    archivePrefix={arXiv},
    eprint={cmp-lg/9605020},
    primaryClass={cmp-lg cs.CL}
}