Synchronic and Diachronic Predictors of Socialness Ratings of Words
In recent works, a new psycholinguistic concept has been introduced and studied that is the socialness of a word. In particular, Diveica et al., 2022 presents a dictionary with socialness ratings obtained using a survey method. The socialness rating reflects word social significance. Unfortunately,...
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National Research University Higher School of Economics
2024-12-01
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| Series: | Journal of Language and Education |
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| Online Access: | https://jle.hse.ru/article/view/22439 |
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| author | Владимир Владимирович Бочкарев Анна Владимировна Шевлякова Андрей Алексеевич Ачкеев |
| author_facet | Владимир Владимирович Бочкарев Анна Владимировна Шевлякова Андрей Алексеевич Ачкеев |
| author_sort | Владимир Владимирович Бочкарев |
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In recent works, a new psycholinguistic concept has been introduced and studied that is the socialness of a word. In particular, Diveica et al., 2022 presents a dictionary with socialness ratings obtained using a survey method. The socialness rating reflects word social significance. Unfortunately, the size of the existed dictionary with word socialness ratings is relatively small. In this paper, we propose linear and neural network predictors of socialness ratings by using pre-trained fasttext vectors as input. The obtained Spearman`s correlation coefficient between human socialness ratings and machine ones is 0.869. The trained models allowed obtaining socialness ratings for 2 million English words, as well as a wide range of words in 43 other languages. An unexpected result is that the linear model provides highly accurate estimate of the socialness ratings, which can be hardly further improved. Apparently, this is due to the fact that in the space of vectors representing words there is a selected direction responsible for meanings associated with socialness driven by of social factors influencing word representation and use. The article also presents a diachronic neural network predictor of concreteness ratings using word co-occurrence vectors as input data. It is shown that using a one-year data from a large diachronic corpus Google Books Ngram one can obtain accuracy comparable to the accuracy of synchronic estimates. We study some examples of words words that are characterised by significant changes in socialness ratings over the past 150 years. It is concluded that changes in socialness ratings can serve as a marker of word meaning change.
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| format | Article |
| id | doaj-art-fe0c5a89ecfc4169b057df2340e1c584 |
| institution | Kabale University |
| issn | 2411-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | National Research University Higher School of Economics |
| record_format | Article |
| series | Journal of Language and Education |
| spelling | doaj-art-fe0c5a89ecfc4169b057df2340e1c5842025-01-07T16:17:14ZengNational Research University Higher School of EconomicsJournal of Language and Education2411-73902024-12-0110410.17323/jle.2024.22439Synchronic and Diachronic Predictors of Socialness Ratings of WordsВладимир Владимирович Бочкарев0Анна Владимировна Шевлякова1Андрей Алексеевич Ачкеев2Kazan Federal University, Kazan, RussiaKazan Federal University, Kazan, RussiaKazan Federal University, Kazan, Russia In recent works, a new psycholinguistic concept has been introduced and studied that is the socialness of a word. In particular, Diveica et al., 2022 presents a dictionary with socialness ratings obtained using a survey method. The socialness rating reflects word social significance. Unfortunately, the size of the existed dictionary with word socialness ratings is relatively small. In this paper, we propose linear and neural network predictors of socialness ratings by using pre-trained fasttext vectors as input. The obtained Spearman`s correlation coefficient between human socialness ratings and machine ones is 0.869. The trained models allowed obtaining socialness ratings for 2 million English words, as well as a wide range of words in 43 other languages. An unexpected result is that the linear model provides highly accurate estimate of the socialness ratings, which can be hardly further improved. Apparently, this is due to the fact that in the space of vectors representing words there is a selected direction responsible for meanings associated with socialness driven by of social factors influencing word representation and use. The article also presents a diachronic neural network predictor of concreteness ratings using word co-occurrence vectors as input data. It is shown that using a one-year data from a large diachronic corpus Google Books Ngram one can obtain accuracy comparable to the accuracy of synchronic estimates. We study some examples of words words that are characterised by significant changes in socialness ratings over the past 150 years. It is concluded that changes in socialness ratings can serve as a marker of word meaning change. https://jle.hse.ru/article/view/22439SocialnessPsycholinguisticsPsycholinguistic data basesPre-trained word vectorsNeural networksLexical semantic change |
| spellingShingle | Владимир Владимирович Бочкарев Анна Владимировна Шевлякова Андрей Алексеевич Ачкеев Synchronic and Diachronic Predictors of Socialness Ratings of Words Journal of Language and Education Socialness Psycholinguistics Psycholinguistic data bases Pre-trained word vectors Neural networks Lexical semantic change |
| title | Synchronic and Diachronic Predictors of Socialness Ratings of Words |
| title_full | Synchronic and Diachronic Predictors of Socialness Ratings of Words |
| title_fullStr | Synchronic and Diachronic Predictors of Socialness Ratings of Words |
| title_full_unstemmed | Synchronic and Diachronic Predictors of Socialness Ratings of Words |
| title_short | Synchronic and Diachronic Predictors of Socialness Ratings of Words |
| title_sort | synchronic and diachronic predictors of socialness ratings of words |
| topic | Socialness Psycholinguistics Psycholinguistic data bases Pre-trained word vectors Neural networks Lexical semantic change |
| url | https://jle.hse.ru/article/view/22439 |
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