Using known words to learn more words: A distributional model of child vocabulary acquisition
Using known words to learn more words: A distributional model of child vocabulary acquisition
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使用已知单词学习更多单词：儿童词汇习得的分布模型
DOI：
10.1016/j.jml.2023.104446
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发表时间：
2023
影响因子：
4.3
通讯作者：
Willits, Jon A.
中科院分区：
文献类型：
--
作者：
Flores, Andrew Z.;Montag, Jessica L.;Willits, Jon A.
为什么孩子比其他孩子先学会一些单词呢？大量的行为研究已经确定了促进单词学习的语言环境的特性，强调了建立在儿童先前知识基础上的特别信息性的语言环境的重要性。然而，这些发现并没有为利用单词的分布特性来预测词汇构成的研究提供信息。在目前的工作中，我们引入了一个强调先前知识作用的单词学习预测因子。我们使用大量儿童导向语料库中的分布统计量的词汇特性来研究词汇发展中的基于项目的可变性。与之前的分析不同，我们预测了儿童各个年龄段的词汇轨迹，揭示了词汇发展的趋势，这些趋势在单个时间点可能并不明显。我们还表明，无论一个单词的语法类别如何，预测一个孩子是否知道一个单词的最佳分布预测因素是该单词倾向于与之共现的其他已知单词的数量。
Why do children learn some words before others? A large body of behavioral research has identified properties of the language environment that facilitate word learning, emphasizing the importance of particularly informative language contexts that build on children’s prior knowledge. However, these findings have not informed research that uses distributional properties of words to predict vocabulary composition. In the current work, we introduce a predictor of word learning that emphasizes the role of prior knowledge. We investigate item-based variability in vocabulary development using lexical properties of distributional statistics derived from a large corpus of child-directed speech. Unlike previous analyses, we predicted word trajectories cross-sectionally across child age, shedding light on trends in vocabulary development that may not have been evident at a single time point. We also show that regardless of a word’s grammatical class, the best distributional predictor of whether a child knows a word is the number of other known words with which that word tends to co-occur.