Just Accepted
Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach
Authors: 
Jingrui Hou, Shitou ZhangAuthors Info & Claims
Accepted on 04 June 2024
Abstract
Classical Chinese poetry, as an essential aspect of cultural heritage, exhibits rich theme diversity often overlooked in natural language processing research. To address this gap, we aim to explore the classification of thematic categories within this literary domain. We curate a dataset of 2,918 annotated poems spanning seven common themes and propose a BERT-based ensemble learning approach for effective classification. Although this method integrates existing models, it achieves an accuracy and F1 score of over 72% in the 7-class task, surpassing established baselines, and providing a baseline for future research. The experimental findings reveal the effectiveness of ensemble strategies in improving individual base model performance and highlight the potential of the MLP-based ensemble technique. The study contributes to a deeper understanding of thematic categories and textual features in classical Chinese poetry, and offers an automated classification system for classical Chinese poems.
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Index Terms
- Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-enhanced Ensemble Learning Approach
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Association for Computing Machinery
New York, NY, United States
Publication History
Online AM: 07 August 2024
Accepted: 04 June 2024
Revised: 30 April 2024
Received: 26 May 2023
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