Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary

TL;DR

This paper introduces an automatic method to generate large, high-quality concreteness/abstractness dictionaries for words, reducing the need for extensive expert assessments and outperforming existing methods.

Contribution

A novel automatic ranking approach for concreteness that minimizes expert input and achieves comparable or better quality than manual dictionaries.

Findings

High correlation between predicted and expert ratings

Method outperforms state-of-the-art approaches

Effective extrapolation from small expert samples

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.