A Computational Cognitive Model of Novel Word Generalization
نویسندگان
چکیده
A key challenge in vocabulary acquisition is learning which of the many possible meanings is appropriate for a word. The word generalization problem refers to how children associate a word such as dog with a meaning at the appropriate category level in a taxonomy of objects, such as Dalmatians, dogs, or animals. We present the first computational study of word generalization integrated within a word-learning model. The model simulates child and adult patterns of word generalization in a word-learning task. These patterns arise due to the interaction of type and token frequencies in the input data, an influence often observed in people’s generalization of linguistic categories.
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