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arXiv 1006.3271cs.CLphysics.data-anq-bio.NC
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语言习得的概率分析:理论、计算和实验分析

The probabilistic analysis of language acquisition: Theoretical, computational, and experimental analysis

University College London(伦敦大学学院);Centrum Wiskunde & Informatica(数学和计算机科学中心)

Anne S. Hsu, Nick Chater, Paul M. B. Vitanyi

摘要:

关于语言学习在多大程度上是由先天的语言特异性偏见控制的,还是通过一般认知原则获得的,有很多争论。在这里,我们在三个层面上研究了概率语言习得假设:我们概述了一个新的理论结果,表明纯粹从观察语言样本中可以学习到广泛的语言类别的确切生成模型。然后,我们描述了一个最近提出的实用框架,量化自然语言的可学习性,允许特定的可学习性预测,第一次。在以前的工作中,这个框架被用来预测各种各样的语言结构的可学习性,其中可学习性一直有很大的争议。在这里,我们提出了一个新的实验来测试这些可学习性预测。我们发现,我们的实验结果支持的可能性,这些语言结构获得的概率从认知的一般原则。

英文摘要:

There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact generative model underlying a wide class of languages, purely from observing samples of the language. We then describe a recently proposed practical framework, which quantifies natural language learnability, allowing specific learnability predictions to be made for the first time. In previous work, this framework was used to make learnability predictions for a wide variety of linguistic constructions, for which learnability has been much debated. Here, we present a new experiment which tests these learnability predictions. We find that our experimental results support the possibility that these linguistic constructions are acquired probabilistically from cognition-general principles.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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