Thematic classification of ancient Chinese poetry using TwinEmbedAttentionNet

Abstract

Ancient Chinese poetry, a reflection of Chinas rich cultural and philosophical fabric, encapsulates the evolving socio-cultural nuances of its historical epochs. Despite its cultural significance, there remains an evident lacuna in comprehensively classifying its recurring themes, due in part to the conciseness and polysemy intrinsic to the language and the essentiality of embedded cultural and historical contexts. Addressing this challenge, this study introduces TwinEmbedAttentionNet, a pioneering method tailored for the thematic classification of ancient Chinese poetry. This approach synergistically integrates pretrained word and sentence embeddings with an attention mechanism, ensuring the nuanced representation of the poetrys intricate details. Our results showcase its superior performance over existing models. Furthermore, an in-depth examination of model components offers insights into their respective thematic categorization efficacies. This research not only advances the academic understanding of ancient Chinese poetry but also underscores the potential of innovative neural networks in processing historically rich textual data.