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- W2893366624 abstract "The paper presents a supervised machine learning experiment with multiple features for identification of sentences containing verbal metaphors in raw Russian text. We introduce the custom-created training dataset, describe the feature engineering techniques, and discuss the results. The following set of features is applied: distributional semantic features, lexical and morphosyntactic co-occurrence frequencies, flag words, quotation marks, and sentence length. We combine these features into models of varying complexity; the results of the experiment demonstrate that fairly simple models based on lexical, morphosyntactic and semantic features are able to produce competitive results." @default.
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- W2893366624 date "2018-01-01" @default.
- W2893366624 modified "2023-09-25" @default.
- W2893366624 title "A Multi-feature Classifier for Verbal Metaphor Identification in Russian Texts" @default.
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- W2893366624 doi "https://doi.org/10.1007/978-3-030-01204-5_3" @default.
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