Do topics make a metaphor? Topic modeling for metaphor identification and analysis in Russian.

P. 69–81.

Badryzlova Y., Nikiforova A., Lyashevskaya O.

The paper examines the efficiency of topic models as features for computational identification and conceptual analysis of linguistic metaphor on Russian data. We train topic models using three algorithms (LDA and ARTM – sparse and dense) and evaluate their quality. We compute topic vectors for sentences of a metaphor-annotated Russian corpus and train several classifiers to identify metaphor with these vectors. We compare the performance of the topic modeling classifiers with other state-of-the-art features (lexical, morphosyntactic, semantic coherence, and concreteness-abstractness) and their different combinations to see how topics contribute to metaphor identification. We show that some of the topics are more frequent in metaphoric contexts while others are more characteristic of non-metaphoric sentences, thus constituting topic predictors of metaphoricity, and discuss whether these predictors align with the conceptual mappings attested in literature. We also compare the topical heterogeneity of metaphoric and non-metaphoric contexts in order to test the hypothesis that metaphoric discourse should display greater topical variability due to the presence of Source and Target domains.The paper examines the efficiency of topic models as features for computational identification and conceptual analysis of linguistic metaphor on Russian data. We train topic models using three algorithms (LDA and ARTM – sparse and dense) and evaluate their quality. We compute topic vectors for sentences of a metaphor-annotated Russian corpus and train several classifiers to identify metaphor with these vectors. We compare the performance of the topic modeling classifiers with other state-of-the-art features (lexical, morphosyntactic, semantic coherence, and concreteness-abstractness) and their different combinations to see how topics contribute to metaphor identification. We show that some of the topics are more frequent in metaphoric contexts while others are more characteristic of non-metaphoric sentences, thus constituting topic predictors of metaphoricity, and discuss whether these predictors align with the conceptual mappings attested in literature. We also compare the topical heterogeneity of metaphoric and non-metaphoric contexts in order to test the hypothesis that metaphoric discourse should display greater topical variability due to the presence of Source and Target domains.

Keywords: topic modeling, LDA, Latent Dirichlet Allocation (LDA), тематическое моделирование, metaphor identification, BigARTM, ARTM, Topical Predictors of Metaphoricity, Topical Profiles, Topical Heterogeneity, распознавание метафоры, тематические признаки метафоричности, тематический профиль, тематическая гетерогенность

In book

Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers

Vol. 12602. , Springer, 2021.