论文标题
基于小型人词典的具有具体性/抽象等级的大型词典的自动产生
Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
论文作者
论文摘要
具体/抽象的单词用于越来越多的心理学和神经生理学研究。对于几种语言，已经手动创建了大型词典。这是一个非常耗时且昂贵的过程。为了生成大型的高质量词典/混凝土/抽象词的单词，需要自动推断出在较小样本上获得的专家评估。出现的研究问题是，这样的样本应该有多大的外推。在本文中，我们提出了一种自动排名单词的具体性的方法，并提出了一种显着降低专家评估量的方法。该方法已在大型英语测试集上进行了评估。构造词典的质量与专家的质量相媲美。与最先进的方法相比，预测和专家等级之间的相关性更高。
Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.
