The notions of concreteness and imageability, traditionally important in psycholinguistics, are gaining significance in semantic-oriented natural language processing tasks. In this paper we investigate the predictability of these two concepts via supervised learning, using word embeddings as explanatory variables. We perform predictions both within and across languages by exploiting collections of cross-lingual embeddings aligned to a single vector space. We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20% in correlation when predicting across languages. We further show that the cross-lingual transfer via word embeddings is more efficient than the simple transfer via bilingual dictionaries.

通过有监督学习，利用词嵌入作为解释变量，我们研究了具体性和形象性这两个概念的可预测性。我们利用与单一向量空间对齐的跨语言嵌入的集合在语言内和语言间进行预测。我们发现，具体性和形象性这两个概念在语言内和语言间都具有高度可预测性，跨语言预测的相关性损失最多达到20％。我们进一步展示了通过词嵌入的跨语言传输比简单通过双语词典进行传输更加有效。

通过词向量预测单词的具体性和形象性跨语言和语言内的