Affiliations: [a] Faculty of Computer Science and Software Engineering, Innopolis University, st. Universitetskaya, 1, Innopolis, Republic of Tatarstan, Russian Federation
| [b] Linguistic research and education center, Research laboratory ‘Intellectual technologies of text management’, Kazan Federal University, 2, Kazan, the Republic of Tatarstan, Russian Federation
Correspondence:
[*]
Corresponding author. Vladimir Ivanov, Faculty of Computer Science and Software Engineering, Innopolis University, st. Universitetskaya, 1, Innopolis, Republic of Tatarstan, 420500, Russian Federation; E-mail: [email protected].
Abstract: 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.
