Multidimensional Domain Knowledge Framework for Poet Profiling

Authorship profiling is a subtask of authorship identification. This task can be regarded as an analysis of personal writing styles, which has been widely investigated. However, no previous studies have attempted to analyze the authorship of classical Chinese poetry. First, we provide an approach to evaluate the popularity of poets, and we also establish a public corpus containing the top 20 most popular poets in the Tang Dynasty for authorship profiling. Then, a novel poetry authorship profiling framework named multidimensional domain knowledge poet profiling (M-DKPP) is proposed, combining the knowledge of authorship attribution and the text's stylistic features with domain knowledge described by experts in traditional poetry studies. A case study for Li Bai is used to prove the validity and applicability of our framework. Finally, the performance of M-DKPP framework is evaluated with four poem datasets. On all datasets, the proposed framework outperforms several baseline approaches for authorship attribution.

Automated summary

This study proposes an approach to analyze the authorship of classical Chinese poetry, by evaluating the popularity of poets and building a public corpus for authorship profiling. A novel framework named M-DKPP is proposed, which combines authorship attribution knowledge, text's stylistic features, and domain knowledge from experts in traditional poetry studies. The validity and applicability of the framework are demonstrated through a case study on Li Bai, and its performance is evaluated on four poem datasets, outperforming several baseline approaches for authorship attribution.