Distributional Language Learning: Mechanisms and Models of Category Formation
Distributional Language Learning: Mechanisms and Models of Category Formation
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DOI：
10.1111/lang.12074
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发表时间：
2014-09-01
影响因子：
4.4
通讯作者：
Newport, Elissa L.
中科院分区：
文献类型：
--
作者：
Aslin, Richard N.;Newport, Elissa L.
在过去的15年里，大量证据证实，在婴儿、儿童、成人以及(至少在某种程度上)非人类动物中，都存在一种强大的分布式学习机制。本文简要回顾了这些文献，然后研究了一些必须解决的基本问题，以便任何分布式学习机制在语言领域内有效地运作。特别是，一个天真的学习者如何确定语言输入语料库中存在的类别的数量，以及什么分布线索使学习者能够将单个词汇项分配到这些类别中？与分布式学习和类别(或规则)学习是分开的机制的假设相反，本文认为这两个看似不同的过程--从语言输入中获得特定结构和从语言输入中泛化到新的范例--实际上是一个单一的机制。支持这一单机制假说的证据来自一系列人工语法学习研究，这些研究不仅表明成年人可以仅从分布信息中学习语法类别，而且在学习语料库中经过验证的话语之间的分布信息的特定模式使成年人能够概括到新的话语或限制概括，当未经证实的话语始终不在学习语料库中时。最后，我们回顾了一个关于有无泛化的分布式学习的计算模型，并总结了该模型对语言范畴学习的启示。
In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes-acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars-actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.