Comparison of the Three Algorithms for Concreteness Rating Estimation of English Words
Vladimir Vladimirovich Bochkarev, Stanislav V. Khristoforov, Anna V. Shevlyakova, Valery Dmitrievich Solovyev · Acta Polytechnica Hungarica · 2022
The paper compares three algorithms for concreteness rating estimation of English words.To train and test the models, we used a number of freely available dictionaries containing concreteness ratings.A feedforward neural network is employed as a regression model.Pre-trained fastText vectors, data on co-occurrence of target words with the most frequent ones, and data on co-occurrence of target words with functional words are used as input data by the considered algorithms.One of the three algorithms was proposed for the first time in this article.We provide detailed explanations of which combinations with functional words are the most informative in terms of concreteness ratings estimation for English words.Although the rest two algorithms have already been used for estimation of concreteness ratings, we consider possible ways to update them and improve the results obtained by a neural network.Thuswise, we use stochastic Spearman's correlation coefficient as a criterion for stopping of training.All three algorithms provided good results.The best value of Spearman's correlation coefficient between the value of the concreteness rating and its estimate was 0.906, which exceeds the values achieved in previous works.