Given a sentence such as Mary fascinated/admired Sue because she did great, the verb fascinated leads people to interpret she as referring to Mary, whereas admired leads people to interpret she as referring to Sue. This phenomenon is known as implicit causality (IC). Recent studies have shown that verbs’ causality biases closely correspond to the verbs’ semantic classes, as classified in VerbNet, a lexicon that groups verbs into classes on the basis of syntactic behavior. The current study further investigates the relationship between causality biases and semantic classes. Using corpus data we show that the collostruction strength between verbs and the syntactic constructions that VerbNet classes are based on can be a good predictor of causality bias. This result suggests that the relation between semantic class and causality bias is not a categorical matter; more typical members of the semantic class show a stronger causality bias than less typical members.

中文翻译：

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与解释传递结构的关联预测动词的隐含因果偏差

给定一个句子，例如 Mary fasciated/admired Sue，因为她做得很好，动词 fascinated 会导致人们将她解释为指代玛丽，而受钦佩会导致人们将她解释为指代苏。这种现象被称为隐含因果关系 (IC)。最近的研究表明，动词的因果关系偏差与动词的语义类别密切对应，如 VerbNet 中的分类，VerbNet 是一个根据句法行为将动词分组的词典。目前的研究进一步调查了因果偏差和语义类别之间的关系。使用语料库数据，我们表明动词之间的搭配强度和 VerbNet 类所基于的句法结构可以很好地预测因果关系偏差。这个结果表明语义类别和因果偏差之间的关系不是一个分类问题；语义类中更典型的成员比不太典型的成员表现出更强的因果关系偏差。