Abstract
ABSTRACTThis research in progress presents a deep learning‐based approach to identifying named entities, including geographical locations and poetic imagery in ancient Chinese poetry. By leveraging association rule mining, this study establishes a connection between historical events, spatial‐temporal trajectories of poets, and sociocultural phenomena of the age. From the perspective of digital humanities, this study hopes to be able to provide new evidence for socioeconomic status, cultural openness, or historical events at different ages and to picture a better view of the development of classical Chinese.
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