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

Ch. 3. P. 23–34.

Badryzlova Y., Panicheva P.

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.

Keywords: Sentence-Level Metaphor Identification, Supervised Binary Classification, Feature Engineering, Distributional Semantic Features, Lexical Co-occurrence Features, Morphosyntactic Co-occurrence Features

In book

Artificial Intelligence and Natural Language, 7th International Conference, AINL 2018, St. Petersburg, Russia, October 17–19, 2018, Proceedings

Issue 930. , Switzerland: Springer, 2018.