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This study explores the effectiveness of deep learning techniques in analyzing and predicting the semantics of Chinese directional complements using a BERT-based transfer learning model. The results confirm that the proposed model can predict the meanings of directional complements with high accuracy by capturing the contributions of various sentence components. The study demonstrates that the meanings of directional complements are dynamically constructed through complex interactions among sentence components and validates the effectiveness of the transfer learning methodology utilizing large-scale corpora and semantic annotation data. However, the analysis is limited to four directional complements, and future research should expand the scope and explore ways to apply the findings in Chinese language education. The outcomes are expected to broaden the understanding of the semantic functions of modern Chinese directional complements and contribute to the improvement of relevant educational methods.
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