Abstract
The paper gives an account of a system for automated identification of linguistic metaphor in Russian text. The design of the system is based on the five features: semantic heterogeneity, lexical and morphosyntactic metaphor association, concreteness-abstractness, and topic vectors. Since each of these features is motivated by a specific set of assumptions about the linguistic and the cognitive nature of metaphor, we undertake feature analysis, aiming to reveal possible linguistic and psycholinguistic cues and hence an explanatory model of metaphoricity. Namely, we extract tentative lexical, morphosyntactic, and topical predictors of metaphoricity; we also test the hypotheses of correlation between metaphoricity, on the one hand, and semantic and topical heterogeneity, as well as concreteness, on the other.
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Notes
- 1.
The full version is available here: https://docs.google.com/document/d/1ZHJP1zJ2-sR-mLvhKXSG7o5gusaIzkpXAKH1TS-rd-8/edit?usp=sharing.
- 2.
mclust: https://mclust-org.github.io/mclust/.
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Badryzlova, Y., Lyashevskaya, O., Nikiforova, A. (2022). Automated Metaphor Identification in Russian and Its Implications for Metaphor Studies. In: González, S.R., et al. Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-030-86887-1_8
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