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- W4377971939 abstract "Clustering and topic modelling are among the most widely used methods for knowledge retrieval in computational linguistics. These methods have many applications in cognitive, discursive, psycholinguistic, and stylistic studies. To date, the application of these methods has mostly been limited to one field per study. This work explores the relationship between different branches of linguistics complementing each other in finding effective, valid, and low-time-consuming methods for the knowledge mediation methodology. Namely, in this paper, we explore the correlation between texts devoted to the same theme viewed from different genre perspectives. The procedure is carried out on the KNIME analytical platform. We adopted the workflow using the elbow method and K-means available on the KNIME hub to determine latent topics in a scientific text, historical novel, and science fiction novel. We reviewed four to five latent topics in each text and provided experts’ reasoning for their relevance. The results reported here provide further evidence for the relevance of genre-specific features in knowledge communication. The data presented here highlight that despite genre specificity, correlations in latent topics are evident and can be applied for mediation purposes. We have obtained encouraging results demonstrating that the interdisciplinary approach enriches our tool base in solving nontrivial tasks." @default.
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- W4377971939 date "2023-01-01" @default.
- W4377971939 modified "2023-09-25" @default.
- W4377971939 title "Knowledge Retrieval by Exploring Correlation Between Texts with Different Genre Perspectives" @default.
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- W4377971939 doi "https://doi.org/10.1007/978-3-031-28086-3_1" @default.
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