Nominal compounds such as red wine and nut case display a continuum of compositionality, with varying contributions from the components of the compound to its semantics. This article proposes a framework for compound compositionality prediction using distributional semantic models, evaluating to what extent they capture idiomaticity compared to human judgments. For evaluation, we introduce data sets containing human judgments in three languages: English, French, and Portuguese. The results obtained reveal a high agreement between the models and human predictions, suggesting that they are able to incorporate information about idiomaticity. We also present an in-depth evaluation of various factors that can affect prediction, such as model and corpus parameters and compositionality operations. General crosslingual analyses reveal the impact of morphological variation and corpus size in the ability of the model to predict compositionality, and of a uniform combination of the components for best results.

中文翻译：

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名义化合物的无监督成分预测

诸如红酒和坚果壳之类的标称复合词显示出连续的组合性，从复合词的成分到其语义的贡献各不相同。本文提出了一个使用分布式语义模型进行复合成分预测的框架，评估与人类判断相比，它们在多大程度上捕获了惯用性。为了进行评估，我们引入了包含三种语言人类判断的数据集：英语、法语和葡萄牙语。获得的结果揭示了模型与人类预测之间的高度一致性，表明它们能够整合有关惯用性的信息。我们还对影响预测的各种因素进行了深入评估，例如模型和语料库参数以及组合性操作。