Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary
This addresses the need for efficient, high-quality dictionaries in psychological and neurophysiological research, representing an incremental improvement over existing automated approaches.
The paper tackles the problem of automatically generating large dictionaries of concreteness/abstractness ratings for words, which are costly to create manually, by proposing a method that reduces the need for expert assessments. The result shows that the constructed dictionaries achieve quality comparable to expert ones, with higher correlation to expert ratings than state-of-the-art methods.
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.