Zipfian frequency distributions facilitate word segmentation in context
Zipfian frequency distributions facilitate word segmentation in context
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DOI：
10.1016/j.cognition.2013.02.002
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发表时间：
2013-06-01
期刊：
影响因子：
3.4
通讯作者：
Frank, Michael C.
中科院分区：
文献类型：
--
作者：
Kurumada, Chigusa;Meylan, Stephan C.;Frank, Michael C.
自然语言中的词频遵循高度倾斜的 Zipfian 分布，但这种分布对语言习得的影响才刚刚开始被理解。通常，旨在模拟语言习得的学习实验使用统一的词频分布。我们使用两种人工语言范例（标准的强制选择任务和新的正字法分割任务，其中参与者单击上下文中单词之间的边界）来检查 Zipfian 分布的效果。我们的数据表明，学习者可以在广泛变化的频率分布中稳健地识别单词形式。此外，虽然识别单个单词的性能最好是通过其频率来预测，但 Zipfian 分布有利于上下文中的分词：高频单词的存在为学习者创造了更多机会应用他们的知识来处理新句子。我们发现，在再现这种性能模式方面，实现“分块”的计算模型比“转换查找”模型更有效。 (C) 2013 年由 Elsevier B.V. 出版
Word frequencies in natural language follow a highly skewed Zipfian distribution, but the consequences of this distribution for language acquisition are only beginning to be understood. Typically, learning experiments that are meant to simulate language acquisition use uniform word frequency distributions. We examine the effects of Zipfian distributions using two artificial language paradigms a standard forced-choice task and a new orthographic segmentation task in which participants click on the boundaries between words in contexts. Our data show that learners can identify word forms robustly across widely varying frequency distributions. In addition, although performance in recognizing individual words is predicted best by their frequency, a Zipfian distribution facilitates word segmentation in context: The presence of high-frequency words creates more chances for learners to apply their knowledge in processing new sentences. We find that computational models that implement "chunking" are more effective than "transition finding" models at reproducing this pattern of performance. (C) 2013 Published by Elsevier B.V.