V. Bochkarev, S. Khristoforov, A. Shevlyakova, V. Solovyev
{"title":"Diachronic Analysis of a Word Concreteness Rating: Impact of Semantic Change","authors":"V. Bochkarev, S. Khristoforov, A. Shevlyakova, V. Solovyev","doi":"10.1134/s1995080224600559","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The paper analyses the correlation of change in word concreteness ratings with semantic change. To perform the analysis, we apply a neural network to diachronic data to obtain concreteness ratings of English words. As input to the model, we use co-occurrence statistics with the most frequent words extracted from the Google Books Ngram diachronic corpus. It is shown that the model, initially trained on data averaged over a long time interval, predicts the concreteness ratings with high accuracy (based on the word co-occurrence data in a particular year). The impact of lexical semantic change on the change in the concreteness rating is analyzed using 69 words borrowed from previous works. As the considered cases show, the neural network estimate of the word concreteness rating is very sensitive to changes in semantics. Among the factors that influence changes in the concreteness rating, we reveal the emergence of new meanings of a word, the competition of word meanings related to different parts of speech, the use of a word as a proper name, and the use of the word as a part of collocations. It is shown in the paper that changes in the concreteness rating can (along with changes in other word properties) serve as a marker of semantic change.</p>","PeriodicalId":46135,"journal":{"name":"Lobachevskii Journal of Mathematics","volume":"25 1","pages":""},"PeriodicalIF":0.8000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lobachevskii Journal of Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1134/s1995080224600559","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS","Score":null,"Total":0}
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Abstract
The paper analyses the correlation of change in word concreteness ratings with semantic change. To perform the analysis, we apply a neural network to diachronic data to obtain concreteness ratings of English words. As input to the model, we use co-occurrence statistics with the most frequent words extracted from the Google Books Ngram diachronic corpus. It is shown that the model, initially trained on data averaged over a long time interval, predicts the concreteness ratings with high accuracy (based on the word co-occurrence data in a particular year). The impact of lexical semantic change on the change in the concreteness rating is analyzed using 69 words borrowed from previous works. As the considered cases show, the neural network estimate of the word concreteness rating is very sensitive to changes in semantics. Among the factors that influence changes in the concreteness rating, we reveal the emergence of new meanings of a word, the competition of word meanings related to different parts of speech, the use of a word as a proper name, and the use of the word as a part of collocations. It is shown in the paper that changes in the concreteness rating can (along with changes in other word properties) serve as a marker of semantic change.
期刊介绍：
Lobachevskii Journal of Mathematics is an international peer reviewed journal published in collaboration with the Russian Academy of Sciences and Kazan Federal University. The journal covers mathematical topics associated with the name of famous Russian mathematician Nikolai Lobachevsky (Lobachevskii). The journal publishes research articles on geometry and topology, algebra, complex analysis, functional analysis, differential equations and mathematical physics, probability theory and stochastic processes, computational mathematics, mathematical modeling, numerical methods and program complexes, computer science, optimal control, and theory of algorithms as well as applied mathematics. The journal welcomes manuscripts from all countries in the English language.
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