A Multi-feature Classifier for Verbal Metaphor Identification in Russian Texts

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 930)


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.


Sentence-level metaphor identification Supervised binary classification Feature engineering Distributional semantic features Lexical co-occurrence features Morphosyntactic co-occurrence features 



The contribution to this study by Polina Panicheva is supported by RFBR grant № 16-06-00529.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsMoscowRussia
  2. 2.St. Petersburg State UniversitySaint PetersburgRussia

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