BACKGROUND Given the impact of lexical properties such as valence, arousal, and concreteness in language processing, recent computational methods have been designed to extrapolate these values from different sources, such as word co-occurrence or word association corpora. These methods have been proven to be particularly successful approaches to extract lexical features from word association data. Consequently, valence, arousal and concreteness seem to be represented in word association, and we hypothesize that they might in fact be critical mediators in the process. METHOD To test our hypothesis, we paired the cue and associate words from three databases in three different languages with their valence, arousal and concreteness values. We then conducted linear regression analyses to see if an associate's score in each dimension could be predicted by the scores of its cue word. RESULTS The analyses showed that the score of the cue words in each of the three dimensions was a strong predictor of the scores of their associates in the same dimension. Furthermore, words that were more strongly associated tended to have more similar scores. CONCLUSIONS We showed that across different languages, word association is mediated and can be predicted by concreteness, arousal and valence.

中文翻译：

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价、唤醒和具体介导词联想。

背景技术考虑到语言处理中的词法特性（例如效价、唤醒和具体性）的影响，最近的计算方法已被设计为从不同来源（例如词共现或词关联语料库）推断这些值。这些方法已被证明是从单词关联数据中提取词汇特征的特别成功的方法。因此，效价、唤醒和具体性似乎在词联想中表现出来，我们假设它们实际上可能是这个过程中的关键中介。方法 为了检验我们的假设，我们将来自三种不同语言的三个数据库中的线索和关联词与其效价、唤醒度和具体性值配对。然后我们进行了线性回归分析，看看是否有一个同事' 每个维度的s得分可以通过其提示词的得分来预测。结果 分析表明，提示词在三个维度中的每一个的得分都是其同事在同一维度中得分的有力预测因子。此外，关联性更强的单词往往具有更相似的分数。结论 我们表明，在不同的语言中，单词联想是介导的，可以通过具体性、唤醒度和效价来预测。