A Topic Modeling Web Service for Classical Chinese Poetry With Auxiliary Semantic Information Enhancement

This paper presents a novel web service for topic modeling of Classical Chinese poetry. Classical poems are typically short, which causes data sparsity problems for standard topic models, and this leads to poor service output quality. To solve this, the authors design a new service, and at its core is a new algorithm that combines character embeddings and the TextRank algorithm to construct ranked character distributions, which serve as auxiliary semantic information to enrich the input and alleviate data sparsity. They evaluated the service on two large datasets of Tang and Song poetry, and measured service quality through Coherence and PMI scores. Experimental results demonstrate that the proposed service significantly outperforms mainstream baseline methods in the consistency of topic mining, validating its effectiveness as an efficient cultural computation service.