Overextension—the phenomenon that children extend known words to describe referents outside their vocabulary—is a hallmark of lexical innovation in early childhood. Overextension is a subject of extensive inquiry in linguistics and developmental psychology, but there exists no coherent formal account of this phenomenon. We develop a general computational framework that captures important properties of overextension reported separately in the previous literature. We operationalize overextension as probabilistic inference over a conceptual space that draws on a fusion of knowledge from lexical semantics, deep neural networks, and psychological experiments to support both production and comprehension. We show how this minimally parameterized framework explains overextension in young children over a comprehensive set of noun-referent pairs previously reported in child speech, and it also predicts the behavioral asymmetry in children's overextensional production and comprehension reported in lab settings. Our work offers a computational theory for the origins of word meaning extension and supports a single-system view of language production and comprehension.

过度扩展（儿童过度扩展已知单词以描述其词汇以外的对象）的现象是幼儿期词汇创新的标志。过度扩张是语言学和发展心理学广泛研究的课题，但对这种现象没有统一的形式上的解释。我们开发了一个通用的计算框架，该框架捕获了先前文献中单独报告的过度扩展的重要属性。我们将过度扩展作为概念空间上的概率推断进行操作，该概念空间利用词汇语义，深度神经网络和心理学实验中的知识融合来支持生产和理解。我们将展示这个最小化参数化的框架如何解释幼儿在先前在儿童语音中报告的一组全面的名词指称对上的过度伸展，并且还可以预测在实验室环境中报告的儿童过度伸展产生和理解中的行为不对称。我们的工作为词义扩展的起源提供了一种计算理论，并支持语言生成和理解的单系统视图。