Children show a remarkable degree of consistency in learning some words earlier than others. What patterns of word usage predict variations among words in age of acquisition? We use distributional analysis of a naturalistic corpus of child‐directed speech to create quantitative features representing natural variability in word contexts. We evaluate two sets of features: One set is generated from the distribution of words into frames defined by the two adjacent words. These features primarily encode syntactic aspects of word usage. The other set is generated from non‐adjacent co‐occurrences between words. These features encode complementary thematic aspects of word usage. Regression models using these distributional features to predict age of acquisition of 656 early‐acquired English words indicate that both types of features improve predictions over simpler models based on frequency and appearance in salient or simple utterance contexts. Syntactic features were stronger predictors of children's production than comprehension, whereas thematic features were stronger predictors of comprehension. Overall, earlier acquisition was predicted by features representing frames that select for nouns and verbs, and by thematic content related to food and face‐to‐face play topics; later acquisition was predicted by features representing frames that select for pronouns and question words, and by content related to narratives and object play.

中文翻译：

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相邻和非相邻词上下文都可以预测英语单词的习得年龄：面向儿童的语音的分布语料库分析

儿童在学习某些单词时比其他单词更早地表现出显着的一致性。哪些词的使用模式可以预测习得年龄中词之间的变化？我们使用面向儿童的自然语料库的分布分析来创建表示单词上下文中自然可变性的定量特征。我们评估两组特征：一组是从将单词分布到由两个相邻单词定义的帧中生成的。这些功能主要编码单词使用的句法方面。另一组由单词之间的非相邻共现生成。这些特征编码了单词使用的互补主题方面。使用这些分布特征来预测 656 个早期英语单词的习得年龄的回归模型表明，这两种类型的特征比基于显着或简单话语上下文中的频率和外观的简单模型改进了预测。句法特征是比理解力更强的儿童生产预测因子，而主题特征是更强的理解力预测因子。总体而言，通过代表选择名词和动词的框架的特征以及与食物和面对面游戏主题相关的主题内容来预测早期习得；后来的习得是通过代表选择代词和疑问词的框架的特征，以及与叙述和物体游戏相关的内容来预测的。