Abstract
The aim of this paper is to determine the main features of word forms that undergo reduction in Russian spontaneous speech using machine learning algorithms. We examined the factors proposed in the previous corpus-based studies, namely, the number of syllables, word frequency, part of speech, reduction of the preceding word. We used the texts from the Corpus of Russian Oral Speech as the data. The following machine learning algorithms were applied: extremely randomized tree, random forest and logistic regression. The results show that the higher the frequency of a word form is, the higher the chances are that it will be reduced; the more syllables a word form has, the higher probability of its reduction is. But we did not find the influence of the factor whether the previous word is reduced or not. Regarding the part-of-speech feature, the results are not that straightforward since we received different lists using different algorithms, but the adjective and parenthetical word were in both of them. Thus, we can conclude that adjectives and parenthetical words are likely to be reduced more often than other parts of speech.
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The research is supported by the grant #19-012-00629 from the Russian Foundation for Basic Research.
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Dayter, M., Riekhakaynen, E. (2020). Automatic Prediction of Word Form Reduction in Russian Spontaneous Speech. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham. https://doi.org/10.1007/978-3-030-60276-5_12
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