M ing O fficial: A Ming Official Career Dataset and a Historical Context-Aware Representation Learning Framework (2023)

Abstract: In Chinese studies, understanding the nuanced traits of historical figures, often not explicitly evident in biographical data, has been a key interest. However, identifying these traits can be challenging due to the need for domain expertise, specialist knowledge, and context-specific insights, making the process time-consuming and difficult to scale. Our focus on studying officials from China’s Ming Dynasty is no exception. To tackle this challenge, we propose MingOfficial, a large-scale multi-modal dataset consisting of both structured (career records, annotated personnel types) and text (historical texts) data for 9,376 officials. We further couple the dataset with a a graph neural network (GNN) to combine both modalities in order to allow investigation of social structures and provide features to boost down-stream tasks. Experiments show that our proposed MingOfficial could enable exploratory analysis of official identities, and also significantly boost performance in tasks such as identifying nuance identities (e.g. civil officials holding military power) from 24.6% to 98.2% F _1 score in hold-out test set. By making MingOfficial publicly available (see main text for the URL) as both a dataset and an interactive tool, we aim to stimulate further research into the role of social context and representation learning in identifying individual characteristics, and hope to provide inspiration for computational approaches in other fields beyond Chinese studies.

## introduction investigating political figures has been a key interest in chinese historical research. techniques such as prosopography, which systematically collates all relevant biographical data, have been employed to the numbers following the author names indicate their order of authorship. c corresponding author. email: thtsai@g.ncu.edu.tw examine shared histories of past groups or individuals @xcite @xcite . recently with the trend of digital humanities, databases such as cbdb (harvard @xcite have brought a transformative shift in this field, offering scholars and historians a more flexible approach to studying chinese political figures @xcite . these databases allows answering queries like "who entered government through the civil examination from jiangnan region during the hongzhi reign (1487-1505)?" and have proven useful for studying officials in china's ming dynasty, which is our focus in chinese study. however, identifying nuanced characteristics not explicitly presented in biographical data remains challenging. for example, the possession of military power by civil officials -an official with a position in the war ministry (兵部), a department overseeing military administration @xcite , in the career record does not necessarily having military power unless textual record suggests military campaigns under the official's command. identifying these cases often requires specialized knowledge and contextual understanding. undoubtedly, such process is time-consuming. to tackle this challenge, we hypothesize that alongside career records, an approach considering social relations among civil officials along with their notable activities described in records, would provide much needed insights. in doing so, we propose mingofficial, a large-scale multi-modal dataset consisting of two modalities: (1) structured data, the career records of 13, 031 ming dynasty officials, reflecting their political standing and social prestige, and (2) textural data, 69, 688 paragraphs from the records in ming shi lu (明實錄, or imperial annals of ming dynasty), documenting major activities an official participated in. this data, organized into a graph structure, is coupled with graph neural networks (gnn) @xcite to embed historical context and social dynamics into a lower-dimensional space, enhancing downstream tasks. we empirically analyze the dataset and apply it to down-stream tasks. the dataset in its vanilla form already could reflect the changing power dynamics over time and shows the latent social relations among officials. furthermore, our graph neural network model enhances the identification of nuanced traits, boosting the f 1 score from 24.6% to 98.2% in distinguishing civil officials with military power. this substantial improvement is achieved despite being trained on a mere 6% of labeled data, composed of only 49 instances, with just 8 positive cases. this demonstrates the practicality of our approach for tasks identifying character traits, which often require costly annotations entailing meticulous reading of scattered historical records about an individual. the potential of mingofficial extends beyond its historical implications. it serves as a comprehensive knowledge base for numerous research fields, including network analysis, sociological studies, computational linguistics, and natural language processing. it also provides valuable insights into the ming dynasty's political and social dynamics, forming a strong foundation for comparative historical and political studies. our contributions are the following: ## the mingofficial dataset in this section, we discuss the challenges of identifying nuanced characteristics of civil officials' military power, the sources and preprocessing methods for our dataset, and the process of reliability of annation in our dataset. ## graph-based representation learning an important part of our proposed method, depicted in figure 4 , involves graph-based representation learning. concretely, we start with the construction of the graph and node initialization, using information in the mingofficial dataset. we then apply graph neural networks (gnn) @xcite to learn the officials' representations. finally, we utilize these learned representations to identify the military power of civil officials. ## experiments in this section we start with our technical setup, and show exploratory analysis of mingofficial, followed by the results and analysis of our method. ## conclusion and future work in this work, we introduce a dataset, derived from career records of the officials in the ming dynasty and their relevant historical texts in ming shi lu (明 實錄, or imperial annals of ming dynasty). our mingofficial dataset captures the dynamic social structures among officials throughout the reigns of various ming emperors. in addition, we took the first step to study the officials in the past with a graph-based representation learning approach. our proposed method underscores the potential of integrating the literary context of historical research in a less direct yet effective manner. instead of depending heavily on the straightforward interpretation of the contextual meaning of historical research, this approach allows for an indirect understanding through relationships that are much easier to extract from the text. this method addresses a significant challenge: minimizing the dependency on labor-intensive interpretation while integrating the rich context from historical texts. experiments shows that our approach can learn effective representation to discern nuanced characteristics of individuals such as civil official's military power in the ming dynasty, despite being trained on only 49 labeled instances, with just 8 positive cases. this finding highlights the potential to significantly reduce annotation labor in future applications despite the advancements achieved, several potential research areas remain unexplored due to the study's focused scope, which we earmark for future work. for example, we envision a future extension on extracting local-scale, fine-grained relationships between officials in targeted groups that are not explicitly documented in historical documents. another prospective extension is applying our approach to other historical studies, such as examining other governmental systems (for instance, the byzantine empire) with complex and multimodal historical recordings. lastly, we foresee opportunities in leveraging our approach to facilitate various other domains, like sociology and political science, thereby further broadening the applicability and impact of our research.