Di Natale Anna, Pellert Max, Garcia David
Center for Medical Statistics, Informatics and Intelligent Systems, Medical Univeristy of Vienna, Inffeldgasse 16c/I, Graz, 8010 Austria.
Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16c/I, Graz, 8010 Austria.
Affect Sci. 2021 May 15;2(2):99-111. doi: 10.1007/s42761-021-00033-1. eCollection 2021 Jun.
Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.
The online version contains supplementary material available at 10.1007/s42761-021-00033-1.
词汇共现是一种语言现象，当多种概念在一种语言中由同一个词表达时就会出现。词汇共现模式经常被用来估计词之间的语义相似度，但关于这些模式之间存在关联的假设仍缺乏大规模的直接实证验证。在此，我们首次表明，由词汇共现模式关联的词具有相似的情感意义。利用已有的翻译数据，我们扩展了词汇共现数据库以涵盖更长的词表。我们通过一种情感词典扩展的无监督方法实现了这一点，该方法利用词汇共现网络数据来内插原始词典中未包含的词的情感评分。我们发现基于网络的估计与实证情感评分之间存在正相关，这表明词汇共现网络包含与情感意义相关的信息。最后，我们将我们的网络方法与在大型语料库上训练的最先进机器学习方法进行比较，结果表明我们简单的基于语言学的无监督算法具有相当的性能且具有高可解释性。这些结果表明，当有额外数据可用时，比如在词汇共现网络中，有可能自动扩展情感规范词典以涵盖详尽的词表。
在线版本包含可在10.1007/s42761-021-00033-1获取的补充材料。
