MingOfficial: A Ming Official Career Dataset and a Historical Context-Aware Representation Learning Framework
Authors: You-Jun Chen
Hsin-Yi Hsieh
Yu Tung Lin
Yingtao Tian
Bert Chan
Yu-Sin Liu
Yi-Hsuan Lin
Richard Tzong-Han Tsai
Venue: EMNLP 2023 Main (2023)
Keywords: graph representation learning
graph neural network
Ming Dynasty
TL;DR: This study presents MingOfficial, a large-scale dataset with an integrated Graph Neural Network to identify individual nuanced characteristics.

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.

MingOfficiall: A Ming Official Career Dataset and a Historical Context-Awand Representation Learning Framework
You-Jun Chen'r, Hsin-Yi Yi Hsieh"; 1 Yu-Tung Lin' Yingtao Tian 2 Bert Wang-Chak Chan??
Yu-Sin L Yi-Hsuan Lin and Richard Tzong- Tsai
'Deparment of Computer Science and Information Engineering, National Central University, Taoyuan. Taiwan
"Courant Institute of Mathematical Sciences, New York University, NY, USA
Center for GIS, RCHSS, Academia Sinica, Google DeepMind
yc7093@nyu. /hsimmosyi, judy502203, yusinliu, julialin, thtsaij @ g.ncu.edu.tv falantian, bertchan google.com
Abstract
MingOfficial
In Chinese studies, understanding the nuanced traits of historical figures, often not explicitly evident in biographical data, has been a key in- terest. However, identifying these traits can be challenging due the need for domain exper- tise, specialist knowledge, and context-specific insights, making the eprocess time-consuming and difficult scale. Our focus studying officials from China's Ming Dynasty is no ex- ception. To tackle this challenge, we propose MifgOfficial, large-scale multi-modal dataset consisting of both structured (career records, annotated personnel types) and text (historical texts) data for 13, 031 officials. We further cou- ple the dataset with a graph neural network (GNN) combine both modalities in order to allow investigation of social structures and provide features boost down-stream tasks. Experiments show that proposed MffgOfff- cial could enable exploratory analysis of offi- cial identities, and significantly boost per- formance in tasks such as identifying nuance identities (e.g. civil officials holding military power) from 24.6% to 98.2% F, in hold- out test set. By making MingOfficial publicly available at https: //data, deposi tar. io/ en en/dataset/mingg tetataset/ming_oofiicoo official both dataset and an interactive tool, we aim to stimulate fur- ther research into the role of social context and representation learning in identifying individ- ual characteristics, and hope to provide inspi- ration for computational approaches in other fields beyond Chinese studies.
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Figure 1: Overview of our approach: We start by propos- ing dataset that combines structured career records and textual historical records of officials from the Ming Dynasty. These diverse sources of information are tegrated using Graph Neural Network (GNN)- based model to aggregate multi-modal data into unified em- beddings. The learned representation captures the nu- anced relations between officials, allowing high perfor- mance downstream tasks qualitatively and quantitatively Ming history study.
1 Introduction
Investigating political figures has been key inter- est in Chinese historical research. Techniques such prosopography, which systematically collates all relevant biographical data, have been employed to
examine shared histories of past groups or individ-
uals (Stone, 1971; Verboven et al., 2007; Lunding et al., 2020). Recently with the trend of digital hu- manities, databases such as CBDB (Harvard Uni-
The numbers following author names indicate their order authorship.
Corresponding author. Email: thtsai @ g.w.iee..nnuu..tw

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versity et al., 2019) have brought transformative shift in this field, offering scholars and historians a more flexible approach to studying Chinese politi- cal figures (Tsui and Wang, 2020). These databases allows answering queries like "Who entered gov- ermment through the civil examination from Jiang- nan region during the Hongzhi reign (1487-1505)?" and have proven useful for studying officials in China's Ming Dynasty, which is our focus in Chi- nese study.
cases. This demonstrates the practicality of our ap- proach for tasks identifying character traits, which often require costly annotations entailing meticu- lous reading of scattered historical records about an individual.
The potential of MingOfficial extends beyond its historical implications. It serves comprehen- sive knowledge base for numerous research fields, including network analysis, sociological studies, computational linguistics, and natural language pro- cessing. It also provides valuable insights into the Ming dynasty's political and social dynamics, form- ing strong foundation for comparative historical and political studies. Our contributions are the following:
However, identifying nuanced characteristics not explicitly presented in biographical data remains challenging. For example, the possession of mil- itary power by civil officials - an official with a position in the War Ministry (FF department overseeing military administration (Hucker, 1998), in the career record does not necessarily having mil- itary power unless textual record suggests military campaigns under the official's command. Identi- fying these cases often requires specialized knowl- edge and contextual understanding. Undoubtedly. such process is time-consuming.
We are the first to use both career records and relevant textual content to represent past political figures.
We introduce MingOfficial, a dataset that in- cludes Ming dynasty officials' career records, position categories, annotated personnel types, and related historical texts, providing a deep understanding of the political network and governmental system of Eastern institutions.
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: (I) structured data, the career records of 13, 031 Ming dynasty of- ficials, reflecting their political standing and social prestige, and (2) textural data, 69, 688 paragraphs from the records in Ming Shi Lu [HJ #8, or Impe- rial Annals of Ming Dynasty), documenting major activities an official participated in. This data, orga- nized into a graph structure, is coupled with graph neural networks (GNN) (Scarselli et al., 2009) to embed historical context and social dynamics into a lower-dimensional space, enhancing downstream tasks.
We propose to learn the representation of Ming officials using graph neural network, fully exploiting the rich, interconnected infor- mation in MingOfficial.
We apply these representations to identify nu- anced characteristics, using the military power of civil officials as an example, and offer an- notation guidelines for future research. Our approach establishes a foundation for future research to further uncover intricate character- istics of past political figures.
The MingOfficial Dataset
In this section, we discuss the challenges of identi- fying nuanced characteristics of civil officials' mili- tary power, the sources and preprocessing methods for our dataset, and the process of reliability of annation in our dataset.
We empirically analyze the dataset and apply to down-stream tasks. The dataset in its vanilla form already could reffect the changing power dy- namics over time and shows the latent social rela- tions among officials. Furthermore, our graph neu- ral network model enhances the identification of nuanced traits, boosting the F1 score from 24.6% to 98.2% in distinguishing civil officials with military power. This substantial improvement achieved despite being trained on a mere 6% of labeled data, composed of only 49 instances, with just positive
2.1 Nuanced Characteristics as a Challenge
Identifying characteristics of historical figure can be challenging due the often implicit nature of such information in biographical data. One exam- ple is the military power wielded by civil officials, as a substantial but not all of them constitutes is proficient in both civil and military affairs (Filipiak, 2012). Correctly identifying these individuals re-

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quires deep domain knowledge and an extensive un- derstanding of the historical backdrop of the Ming Dynasty.
Number of Related Trouts
1..0%%
Pensonnel Type
600
officill
11%
For example, Yu Qian (T F. a civil official whose career records are presented in Figure 1, held a significant position in the War Ministry (Hucker, 1998). Merely having such role does not unequiv- ocally imply military power, as the ministry mainly dealt with logistics, personnel, finances, and infras- tructure rather than active commanding. However, historical accounts, as in Figure I, provide context showing that Yu Qian was tasked with arranging the army to protect the capital during a war event, thus suggesting his military power.
others
military officer
eunuch
Imperial family
(AD)
Number of Officcalls
Number of Officiall
5o00
2000
6000
4000
2000
1000
1000
A.R. R Number of Releted Texts
A Number of Carcer Records
Figure 2: Statistics of the MingOfficial Dataset. (a) Distribution of personnel types. (b) Anmual count of text entries. (c) Distribution of the number of related text entries per official. (d) Distribution of the number of career records per official.
2.2 Dataset Construction
The construction of dataset involves collection and processing from two kinds of data source: struc- tured data and textual data, which we detail as follows.
# 1368- to the Chongzhen Emperor (# , 1627-1644). They include imperial edicts, laws, and major political, economic, and cultural events, serving as a fundamental resource for Ming history research.
Structured Data We gather personal information on 13, 031 officials who served in the Ming Dy- nasty from biographical database (IHP).including names, government entry modes, and raw career records. Given the variance in descriptions of iden- tical positions, we organize these records into 123 categories according to Da Ming Hui Dian (K or The Code of the Ming Dynasty) and Chinese- Chinese-English Dictionary of Ming Government Official Titles (Zhang al., 201). This catego- rization reflects the administrative structure of the Ming government, grouping titles of core officials and subordinates from different departments into office categories, facilitating the conversion of ca- reer information into graph structure. Examples of this categorization are available in Appendix A. Each official's personnel type is inferred from the position categories in their career records, with five groups: civil officials, military officers, eu- nuchs, imperial family members, and others. Fig- ure (a) shows the personnel types distribution among all Ming officials, with civil officials form- ing the majority within the office system. Note that our method effectively handles such imbalanced labels, as demonstrated in Section 4.
We first process the texts in Ming Shi Lu using named entity recognition (Lai, 2016) and named entity disambiguation (Wu, 2021) to identify peo- ple, location names, and official titles in the text. We then select only the paragraphs in which per- son entity is linked obtained total of 69,688 processed texts. The number of processed texts each year shown in Figure 2b, and a breakdown by each emperor is detailed in Appendix B.
ll # #.
official. As a result, we
2.3
The Process and Reliability of Annotation for Civil Officials' Military Power
Identifying military power among civil officials presents unique challenge, requiring an under- standing of both textual content and societal struc- ture. The process we developed, grounded in histor- ical context, could generalize to modern scenarios. serves as a model for identifying specific charac- teristics in complex social networks.
We determine a civil official's military power based on evidence from primary historical sources.
We source related texts from Ming Shi Lu HR. or Imperial Annals of Ming Dy- nasty), collection of thirteen historical books by Ming officials, available at Academia Sinica (2019). These documents cover the reigns of the thirteen Ming Emperors, from the Hongwu Emperor +
Textual Data
These sources include Ming Shi Lu, Ming Shi (UR "i or History of Ming), and the Military System subdivisions of the Economic series in Gujin Tushu Jicheng 4 or Imperial Encyclopae- dia). Officials are identified as having military power if they meet one or more of the following

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3.1 Graph Construction and Node Initialization
FENRAR t E PAREE
Prince of Ning Chen Haoı rebelled. In July, Wang Shou Reny raised troops, lought against Poyang Lake, and captured him.
We construct graph to represent Ming dynasty's complex political landscape, consisting of three node types and four kinds of relations. These nodes represent Ming officials (P), official position cate- gories (O), and relevant historical texts (T). Among the relationships, P-O, P-T. coP-P depict an offi- cial and the office they held (political influence), an official and texts that mention (historical events), and two officials concurrently cited within the same text, respectively (inter-official association).
Figure 3: Example of evidence indicating military power for the official Wang Shou Ren (EFLL)
Annotators
AB
BC
AC
Cohen's 's Kappa
0.80
0.80
0.72
Table inter-annotator agreement table, demonstrating substantial consensus among annotators.
Distinctively, simP-P is dynamically generated during the learning process of the GCNs. At each step, after updating the official embeddings with text embeddings using the P-T relation, officials with the top-k Cosine similarity between their em- beddings form the simP-P relationship, signifying similarities in their experiences as inferred from texts. Except for coP-P, all relationships are de- lineated by reign periods, marking the temporal aspect of an official's 's activity or relationships.
criteria: (1) Personally leading soldiers the bat- tlefield, (2) Commanding troops and (3) Arranging the personnel in the military.
Should a source paragraph detail military en- gagements, be they wars rebellions, deem the associated official to hold military power, based on the given criteria. Consider Figure 3 as an illus- tration: it depicts Wang Shou Ren (also known as ESC or E (f,, a civilian authority, mobiliz- ing troops to suppress a rebellion. This narrative provides clear evidence of his military prowess.
We initiate the nodes embedding as follow. For T, use the CLS sentence embeddings generated by the pretrained vlin et al., 2019), while for P and O, we use the metapath2vec model (Dong et al., 2017) that sam- ples path to reflect the administrative structure the Ming governmental institutions (see Figure 5).
Serr--base-Chiness model (De-
To assess the reliability of our annotations, we employ Cohen's kappa measure (Cohen, 1960) for inter-annotator agreement (IAA) (Artstein, 2017). This assessment involves 100 sampled civil offi- cials from MingOfficial. Our annotators, proficient in literary Chinese and knowledgcable in history and humanities, included an author of the paper(A) and two individuals (B and C) not involved in cri- teria development. Table 1 shows their substan- tial agreement, validating the reliability of our ap- proach.
3.2 Representation Learning
Officials" representations are learned using GNN which update node representations by aggregating information from each node's neighbors to update, effectively generating context-rich feature repre- sentation of the node. The learning includes two stages:
Stage 1: Personnel Type Categorization. The model's objective to classify officials from the Ming Dynasty into three major political groups: civil officials, military officers, eunuchs. It relies on analyzing their career records, with central em- phasis on P-O, encompassing all their held official positions. The model employs GNN aggregate information from O through P-O. Following this, we feed the embedding of (represented as pi) into a feed-forward neural network (FFNN) with softmax (f) to predict the personnel type c, Cross entropy loss is used:
3 Graph-based Representation Learning
An important part of our proposed method, de- picted in Figure 4, involves graph-based represen- tation learning. Concretely, we start with the con- struction of the graph and node initialization, using information in the MingOfficial dataset. We then apply Graph Neural Networks (GNN) (Scarselli et al., 2009) to learn the officials' representations. Finally, we utilize these learned representations to identify the military power of civil officials.
LPT = Σ
log f(i(p:))
(1)
VESofe

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Input
Representation Learning
Official
Title
X
Personnel Type Categorization
Detection Miltary Power of Civil Official
Title
Xp
Ming Officer
Career Information
Aode Intotitation
GNN
Rp
'lmp)
Ming Shi-Lu
Text
2L 08 M1 1B
Braph Construction
Figure 4: Our overall approach to detect the civil official's military power.
Txce
A:Central Government
4.1 Technical Setup
S.owenment
5- GGvuenmen Ofice
Models for Comparison We compare model with non-GNN baseline of Multi-layer Percep- tron (MLP) model and with GNN alternatives, in- cluding GraphSAGE Hamilton et al. (2018), GAT Veličković et al. (2018). and HGT (Hu et al., 2020a) models using different combinations of re- lations. The specific structure of the MLP model, details regarding node initialization and model im- plementation can be found in Appendix F.
Bevenment Ageny Diicaa
(#
C1
C2
C3
Misietiee (h160)))
POCEABCOP
O: Miristor War (AiM
Figure Our GNN based method samples paths for metapath2vec part, using the governmental organization structure in the Ming Dynasty. We designe the hierarchy of nodes of type A, B, C, following the actual govern- ment bierarchy in Ming Dynasty.
Data Splits We split our dataset into training. validation, and testing sets across two stages representation learning (see 3.2). as outlined in Ta- ble 2. In Stage 2, due to the rarity of civil officials with military power and the resource-intensin na- ture of annotation (see E, we have significantly smaller dataset. For this stage, apply 5-fold cross valsdaalio and report the average and stan- dard deviation of model performance across these five sets.
[((lp) = Softmax + b.))
(2)
where Sofo is the of officials for Stage 1, W. and be are learnable parameter of the FFNN. Stage 2: Detection of Civil Officials with Mili- tary Power (MP). Leveraging the parameters of the GNN model trained in the previous stage, the model refines the officials' representations P using P-O and P-P relations. We then feed the embed- ding of P (p:) into another FFNN with softmax (f) predict the probability of P possesing military power (m,). Cross entropy loss used:
4.2 Exploratory Analysis of MingOfficial
We show that our MingOfficial dataset is valuable for examining historical socio-political dynamics, serving potent resource for exploring not only individual biography and prosopography, but also broader social dynamics such as class, mobility. and political networks, emphasizing its value for historical and sociological studies. More details regarding this analysis and relevant historical back- ground can also be found in Appendix C.
LMP Σ VES.iv
log f(m//pi)
(3)
f(m|p) = Softmax(W,, + bm)
(4)
where Saiv is the set of civil officials for Stage 2, W and bm are learnable parameter of the FFNN.
First display the changing prominence of civil officials, military officers, and eunuchs throughout the Ming Dynasty in Figure 6. Overall, this graph showcases the dynamic power shifts be- tween the three major political groups over time. In particular, it provides a preliminary explana-
4 Experiments
this section we start with our technical setup, and show Exploratory Analysis of Ming Official,, followed by the results and analysis of our method.

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Stage
Class
Train
Val
Test
Total
Civil Official (CO)
4,381 (55.1%)
1,096 (13.8%)
1,370 (17.2%)
6,847 (86. (6)
Stage
Military Officer
640 (8.1%)
161 (2.0%)
201 (2.5%)
1002 (12.6%)
Funuch
66 (0.8%)
617 (0.2%)
21 (0.3%)
104 (1.3%)
CO w/o MP
41 (4.9%)
28 (3.4%)
622 (73.8%)
691 (83.1%)
Stage
CO w/ MP
(1.0%)
6 (0.7%)
127 (15.3%)
141 (16.9%)
Table 2: Data Splits for Stage and 2. For Stage 1, sample 64% of the labeled data for training, 16% for validation, and 20% for testing. For Stage 2, we sample 6% for training, 4% for validation, and 90% for testing. The proportion of each class remains consistent across training, validation, and test "CO w/o MP" stands for civil officials not having military power, while "CO w/ MP" stands for civil officials having military power.
Model
Setting
Epoch
F1 (%)
100
IN Eury Otferr
P+O
24.6
MLP
Ganuch
P+0+T
28.85.4
00
P-O
76.72.5
P-O + coP-P
74.52.1
SAGE
P-O + simP-P
87.72.7
200
1315
1330
1300
P-O +
83.22.7
P-O
58.2a.1
Figure 6: The official position power shift among three main groups influential in politics, over the Ming times.
+ coP-P
83.05.1
HGT
4
P-O + simP-P
84.5
P-O + coP-P simP-P
74.33.0
86.01.s
22
P-O + coP-P
91.01.5
GAT
4
P-O + simP-P
98.2g.4
+ coP-P simP-P
96.7o.s
Table 3: Model performance of detecting civil officials with military power under various settings. We report average and standard deviation (as subscripts) perfor- mance over 5 different data splits (see 4.1).
Figure 7: Co-occurrence network during Zhengtong (AD 1449) on left and Jingtai era (AD 1450-1451) on right. Our figure of interest, Yu Qian, is circled in red.
score for CO w/ MP in Table 3.
Efficacy of Graph Structure with GNN All GNN models consistently outperform the baseline MLP model by significant margin. This notable improvement is evident despite the GNN models being trained for only 4 epochs, compared to the 20 epochs for the MLP model. These results underline the effectiveness of our graph-based approach generating high-quality individual representations.
tion of the phenomena whereby military officers were heavily relied upon in the early Ming dynasty, and how the power in military discourse gradually shifted towards civil officials and eunuchs during the middle period.
Then we we construct co-occurrence networks based on the person mentions in texts, and show in Figure the complex and changing social relations spanning across the reigns two different emper- ors. The depicted variations network structures between the two periods offer unique insights into the dynamics of interpersonal relationships and so- cial structures over time.
Impact of Incorporting Textual Data found that integrating textual data, specifically official narratives, significantly enhances the model's per- formance. This trend is consistent across both the MLP and GNN models. For the MLP model, the use of text embeddings resulted in better per- formance than models without them. For GNN models, those incorporating coP-P or(and) simP- along with P-O generally outperform those us- ing P-O alone. The only exception to this trend was observed with SAGE, where P-O + coP-P per-
4.3 Results
We evaluate the GNN and MLP models on the task detecting civil officials with military power (CO w/ MP). We experiment with various settings for these models and present their performance as Fl

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forms worse than P-O alone. We attribute this SAGE's lack of an attention mechanism which could weigh the importance the relatively coarse co-occurrence relations. contrast, GAT and HGT, with their attention mechanisms, can more effec- tively leverage coP-P.
b17
622
622
0
6:2
Thue
Thuc
Truc
27
100
18
109
124
121
Predicted
Predicted
Predicted
Predicted
Figure 8: Confusion matrices of the prediction results of GAT on testing data. From left to right: P-O, P-O coP-P, P-O simP-P, P-O coP-P + simP-P.
Comparison between coP-P and simP-P Across all GNN models, the combination of P-O simP-P consistently achieved the highest scores. Particularly, SAGE and GAT showed considerable improvements when using P-O simP-P as opposed to P-O + coP-P (SAGE: +17.7%, GAT: + 8.0%). In contrast, HGT showed only a marginal increase. We also experimented with the incorporation of both coP-P and simP-P. which improved the performance of SAGE and GAT compared to P-O + coP-P. However, this setting negatively impacted the performance of HGT. Given that HGT treats the graph as heterogeneous entity with different attention layers for various types of nodes, we hypothesize that might require additional training time and data for model optimization.
(P4O+T)
(F-0)
(P-O+siml-P))
Figure 9: Visualization of Learned Representation. points indicate civil officials with military power, and blue points indicate those without.
sources is apparent to enhance accuracy when data is limited.
Direct evidence of military power in related narratives significantly improved prediction accuracy, particularly when using P-O + simP- P. This underscores the importance of integrat- ing text-based evidence.
Best Performance and Training Efficiency No- tably, GAT achieves the most stable and highest Fl score of 98. 2% for CO w/ MP under the setting of P-O simP-P. This outstanding performance achieved on small training set of only 49 examples and with training epochs, highlight- ing the efficiency our approach. This finding is particularly important as it suggests the potential to significantly reduce annotation labor in future applications.
Abundant co-occurrence relations can some- times introduce noise, leading to inaccurate predictions. This was particularly evident cases where officials had numerous related texts, but the co-occurrence did not necessar- ily indicate the presence of military power. This indicates the need for careful manage- ment of such relations in future models.
4.4 Error Analysis
We conducted an error analysis on the best perform- ing GNN model - GAT, and present the confusion matrices for the prediction results under different settings in Figure 8. We glean several important insights as follows, with full details available in the Appendix
4.5 Embedding Visualization
Using UMAP (Melnnes and Healy, 2018) imple- mented through TensorBoard (Abadi et al., 2015). we projected learned representations into 3D space (see Figure 9). It is evident that the GAT (P- O+simP-P) model distinguishes well between offi- cials with and without military power (Figure 9c), with the lower-performing HGT (P-O) also show- ing some differentiation (Figure 9b). Conversely, the MLP (P+O+T) model blends the categories. indicating its limited discrimination ability (Fig- ure 9a). The visualization affirms the advantage of our graph-based based approach for identifying nuanced differences.
Career records alone (P-O) may not be suffi- cient to identify nuanced characteristics, such as the military power among civil officials. In some cases, the model incorrectly predicted these characteristics based on seemingly simi- lar career trajectories.
Sparse individual information consistently caused false negatives across all settings. The need for enriched datasets or additional data

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4.6 Discussion
individual. Following carefully defined selection criteria, prior works (Bol, 2012: Suddaby et al., 2016) model the biographical information in sys- tematic way to identify a group of individuals for understanding how the members of a target group were interconnected or "operated within and upon the institutions social, political, legal, economic, intellectual - of their time (Keats-Rohan, 2000)."
Overall, the experimental results confirm the effec- tiveness of our proposed method in detecting civil officials with military power; however, there are two findings we believe worthy of deliberation:
Chinese Language Model for Initializing Node T Our decision to utilize the BERT-base-chinese model as the encoder was informed by our prior experience, where in our separate studies of Ming Dynasty texts found that models tailored for classi- cal Chinese do not show significant improvements in tasks related text classification that relies heav- ily on the embeddings. We hypothesize this effec- tiveness might be due to the similarity between the writing style of the Ming Dynasty historical texts and contemporary Chinese. Additionally, since our task is more on similarity and co-occurrence, we think that a large enough pretrained model such BERT-base-chinese can produce satisfactory re- sults. We envision that large language model that can properly be trained on both Modern and Classical Chinese would benefit study this kind.
Extraction of biographical information from texts and network Many existing works that also leverage the rich information in biographical databases and historical texts have focused the extraction of biographical information from texts and network analysis. Liu al. (2015), Lai (2016), Yan et al. (2020), and Liu et al. (2021) employ the information in biographical databases to annotate texts, and use the annotated texts to train a lan- guage model for named entity recognition task
identifying persons, locations, and official titles. Armand and Henriot (2021) and Zhang et al. (2021) further use these identified entities construct a latent social network based on co-occurrence of person names. Several works also explore the cor- respondence network (Weerdt et al., 2016), kinship network (Liu and Wang, 2017). 7), and career trajec- tory network (Xiong, 2021).
More Effective Way to Fuse coP-P and simP-P Interactions We suspect the decreased perfor- mance when using both types of relations comes from the long text spans in coP-P relations. Given that these relations are drawn from entire para- graphs, there might be a weak connection between a person mentioned at the beginning and those mentioned at the end of a long paragraph. We hypothesize that using shorter text spans, like sen- tences, might work better and reduce noise. How- ever, segmenting the paragraphs into sentences is challenge in our case because the original records, like most Classical Chinese texts, lack punctuation marks which poses a segmentation challenge, and during our research, we haven't 't identified a tool that could efficiently address this challenge. We think that future research will look into better ways to combine these P-P interactions, and we'll leave this as challenge for future study.
Graph Neural Network (GNN) and Represen- tation Learning GNN utilizes graph structures for message passing, a process that aggregates a node's 's local neighborhood information and update the node's 's representation. Notable GNN architec- tures have been proposed. Kipf and Welling (2017) propose graph convolutional network (GCN), fa- cilitating efficient message passing and aggrega- tion between neighbors. Hamilton et al. (2018) propose GraphS an inductive framework us- ing local neighbor sampling and various aggrega- tions operators including mean, LSTM and pool- ing. Veličković et al. (2018) propose graph atten- tion network (GAT), incorporating attention mech- anism into GNNs and allowing different weights for nodes within the same neighborhood. Hu et al. (2020b) propose heterogeneous graph transformer (HGT), employing meta relations in heterogencous graphs to parameterize shared weight matrices for generalization and integrates with transformer-like attention architecture.
5 Related Work
Prosopography This field of studying common characteristics of targeted population focuses the population's 's shared lives (Stone, 1971; Keats- Rohan, 2007; Gerritsen, 2008): for example, they might hold the same official position, come from a specific status group or economic class, belong to a specific period, or share a connection to a single
Such GNNs have been utilized in contempo- rary political figure representation learning. For instance, (Mou et al., 2021) and (Feng et al., 2022)

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have applied GNNs jointly model legislators and learn representations, incorporating voting behav- ior and public statements or leveraging information from Wikipedia and think tanks, respectively.
future work. For example, we envision future extension on extracting local-scale, fine-grained relationships between officials in targeted groups that are not explicitly documented in historical doc- uments. Another prospective extension applying our approach to other historical studies, such as ex- amining other governmental systems (for instance, the Byzantine Empire) with complex and multi- modal historical recordings. Lastly, we foresee op- portunities in leveraging our approach to facilitate various other domains, like sociology and political science, thereby further broadening the applicabil- ity and impact of our research.
Our proposed dataset and method differs from these related work in that we propose to jointly model the officials, the events from both structured and textual data, and leverage representation learn- ing with graph neural network (GNN) iden- tify nuanced charactcristics of past officials, thus giving a comprehensive view of historical figures not seen before. Additionally, the textual data fea- tures high volume of co-occurrence ties and la- tent associations, which can be further leveraged investigate the interpersonal relationships and political subgroups among past officials. Lastly, our approach also unveils text-based evidence that substantiates the identified nuanced characteristics.
6 Conclusion and Future Work
this work, we introduce a dataset, derived from career records of the officials the Ming Dynasty and their relevant historical texts in Ming Shi Lu (RH A ik or Imperial Anmals of Ming Dynasty). Our MingOfficial dataset captures the dynamic social structures among officials throughout the reigns of various Ming emperors. In addition, first step study the officials in the past with traph-based representation learning approach.
took the
Our proposed method underscores the potential of integrating the literary context of historical re- search in less direct yet effective manner. Instead of depending heavily on the straightforward inter- pretation of the contextual meaning of historical research, this approach allows for an indirect under- standing 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 charac- teristics of individuals such as civil official's mil- itary power in the Ming Dynasty, despite being trained on only 49 labeled instances, with just positive cases. This finding highlights the potential to significantly reduce annotation labor in future applications
Despite the advancements achieved, several po- tential research areas remain unexplored due to the study's 's focused scope, which we earmark for

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Limitations
Lastly, our research relies on certain assump- tions, including the presumption that all significant characteristics and relationships are captured by the database and textual records. If these assumptions fail, our method might overlook key aspects influ- encing an official's acquisition of military power, Future work can address these limitations by test- ing our method on more comprehensive and di- verse datasets, well as exploring mechanisms for model interpretability and validation for assump- tions.
Despite the demonstrated efficacy of pro- posed method in identifying nuanced characteris- ties within a group of people, there are notable limitations. Firstly, our approach has not been con- trasted with other potential methods for identifying target groups. This lack of comparative analysis is primarily due to the scarcity of related work in the field, including no other publicly available datasets that align with MingOfficial' s characteristics.
We consider our present effort as an initial demonstration, a proof of concept that paves the way for more extensive research endeavors in the future. While our current approach was tested on a single dataset, it holds potential for wider appli- cations across diverse datasets. To provide more context, approach is well-suited for probing historical documents like Veritable Records of Veritable Records of Qing (H and Chorography (Ht )). Beyond that, we can also delve into contem- porary archives, especially those that highlight local intricacies and interpersonal dynamics, such records from Taiwan's 's Legislature and Hong Kong's Legislative Council. We believe that our current work is just the beginning. It lays the groundwork for wider and more diverse research down the road.
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Pier-

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A
Official Position Categorization in Data Construction
where the term frequency tf(t, d) is the frequency of term within record, df(t, D,) is measure of how much influence an office provides within a year
In our data construction process, we organize of- fice (positions) into 123 categories that mirror the administrative structure of the Ming Dynasty gov- ermment. This categorization groups the titles of core officials and subordinates from different de- partments into office categories, simplifying the conversion of of career information into a graph structure.
tf(t,d) = =fid
(6)
(d Dy 1
df(t, Day loo
with f = office mention, d record, Dy - set records with in year y, and = Pyyl In practice, df (t, D) is computed as;
As an illustration, we present the top three most frequently appearing position categories for each emperor in Table 4. Detailed categorization can be found in Figure 14 to 22 at the end of the appendix.
log
+1
(8)
N+1
B Breakdown of the Number of Processed texts by each Emperor in the Ming Dynasty
Instead of incorporating the inverse document frequency to diminish the frequency of terms that not only frequently occur in a record but also fre- quently in all of the records within year, we com- pute the document frequency to strengthen it. We reason that the more occurrence of office in mul- tiple records, the more important an office is in the year. To observe the change in importance of different offices over time, we rank each office in a record by tf-df and count the total number of times each office ranks first by year.
Table 5 provides detailed breakdown of the num- ber of processed texts for each emperor during the Ming Dynasty, which spanned from the reign of Hongwu (1368-1398) Chongzhen (1627-1644). For each emperor, the table specifies:
The name of the emperor (both in English and traditional Chinese characters)
The reign period of each emperor in A.D.
The result, displayed in Figure 6, reveals signif- icant trend: in the early Ming, military officers had significant influence. However, by the late 15th cen- tury, the status of civil officials gradually improved and eventually surpassed the others. Moreover, eunuchs also held considerable influence during certain periods.
The total number of texts processed during their reign
The total number of official names identified from the texts of their respective reigns
C Details on Exploratory Analysis of MingOfficial
Overall, this graph showcases the dynamic power shifts between the three major political groups over time. In particular, it provides pre- liminary explanation of the phenomena whereby military officers were heavily relied upon in the early Ming dynasty, and how the power in military discourse gradually shifted towards civil officials and eunuchs during the middle period. This insight suggests that the MingOfficial dataset a valuable resource for understanding the evolving power dy- namics in the Ming dynasty. Detailed analysis of these trends is left as future work.
Insight 1: MingOfficial Reflects the Changing Power Dynamies among Major Political Groups Over Time. Our initial analysis illustrates the dataset's ability to trace the historical shifts in power among major political groups. We aim to observe the changing prominence of civil officials, military officers, and eunuchs throughout the Ming Dynasty.
We employed a term frequency-document fre- quency (tf-df) calculation, inspired by the numeri- cal statistic term frequency-inverse document fre- quency (tf-idf) (Sparck Jones, 1988), where a term corresponds to an office,
Insight 2:
MingOfficial Preserves the Intri- cate Social Relations among Officials. To fur- ther demonstrate the dataset's capacity for depict- ing complex social relations, we construct co- occurrence networks based on the person mentions
(f,f d, D,)) = tt, d) * df((t, Dy)
(5)

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Emperor
No.1
No.2
No.3
Hongwu
Noble Titles
Core Officers
Other Officers Chief Military Com- missions Noble Titles
the Guard Military Commands
Jianwen
Core Officers the Regional Commission
Other Officers in the Chief Military Com- missions
Military
Tainshun
Core Officers Provin- cial in the Administra-
Hanlin Academy
Noble Titles
Commission
Chenghua
Core Officers Provin- cial in the Administra- tion Commission
Other Officers in the Censorate
Sub- toob-refeetural Or- ficers
Hongzhi
Core Officers Provin- cial in the Administra- tion Commission
Hanlin Academy
Sub-prefectural Of- ficers
Zhengde
Core Officers Provin- cial in the Administra- tion Commission
Other Officers in the Censorate
Provincial Surveil- lance Commission
Jiajing
Core Officers Provin- cial in the Administra- tion Commission
Other Officers in the Censorate
Provincial Surveil-
lance Commission
Taichang
Other Officers in the
Grand Secretary
Core Officers
Censorate
Provincial in the Administration Commission
Tianqi
Other Officers the Censorate
Three Preceptors and Three Juniors the Eastem
Officers Provincial
Core
Administration
Palace
Commission
Chongzhen Other Officers in the
Sub-prefectural Of- ficers
Grand Coordinator
Censorate
Table The top three most frequently appearing position categories under each specific emperor in MingOfficial.
Book of Emperor
Reign Period (A.D.)
# Paragraphs
Officials
Hongwu (8ti
1368-1398
2,687
Yongle (*k ())
4402-1424
2,032
$47
Hongxi 6
1424-1425
227
199
Xuande [ (97)
1425-1435
2,447
Zhengtong (7)
1435-1464
5,715
970
Chenghua (27
1464-1487
5,742
1,024
Hongzhi (51
1487-1505
4,142
1,000
Zhengde (TF)
1505-1521
5,042
1,258
Jiajing ())
1521-1567
11,197
2,042
Longqing (82
1567-1572
2,182
735
Wanli (M) ))
1572-1620
15,689
1,721
Taichang ( A))
1586-1620
217
Tianqi (#
(620-1627
5,229
1,012
Chongzhen # )
1627-1644
4,494
1,230
Table Processed Text Count per Emperor: A tally texts processed for each emperor during the Ming Dynasty. "Including the reign of Jingtai (# (0.. 1449-1457) and Zhengtong's second term under regnal name Tianshun ( HH, 1457-1464). Unliie books of all other emperors, this book was compiled after the fall of Ming Dynasty.
in texts and visualize the network with Gephi (Bas- tian et al., 2009), network analysis tool. Fig- ure 7 present these networks, which span across the reigns of two different emperors from 1449 to 1451. Yu Qian prominently featured in both
(highlighted with a red circle).
The distinct communities within the network are indicated by node colors, as detected by the Lou- vain method built into Gephi. Text colors within nodes represent different personnel types: blue for

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civil officials, purple for military officers, green for eunuchs, and orange for the imperial family. The depicted variations in network structures be- tween the two periods offer unique insights into the dynamics of interpersonal relationships and social structures over time.
leadership the authority to manage and com- mand the troops under the control of small number of high-ranking civil officials. (Filipiak, 2012) As the civil officials are thoroughly indoctri- nated in the ritual and principles of good govern- ment written in the Confucian literature, they were less likely to challenge the imperial power, thus strengthening the military governance by the em- peror. (Zhang, 201 I; Robinson, 2017) Moreover, to alleviate the tension between the Ming emperors, and the civil officials and military officers, eunuchs were also assigned to various influential tactical military positions. (Li, 2018) As time progressed, these measures created tension between the three groups, contributing to the country's ills and has- tening the decline of the Ming dynasty.
In summary, the MingOfficial dataset serves as potent resource for exploring not only individual biography and prosopography, but also broader so- cial dynamics such as class, mobility, and political networks. Our examination of potential associa- tions and network changes underscore the intricate social interactions preserved within the dataset, em- phasizing its value for historical and sociological studies.
D Historical Background
E Resource-intensive Annotation for Determining Civil Officials' Military Power
this section, we will give brief overview of the governmental system in the Ming dynasty and the interaction of officials operating within it.
Determining whether a civil official had actual mil- itary power during their career is complex task that requires significant investment of time and resources. The process cannot solely rely reer records in the database of names and biogra- phies (A & records often lack sufficient detail. For instance, while many Ministers of War led armies in battle, this was not a uniform practice across all reigns during the Ming Dynasty.
D.1 The Ming Government System
The governmental system matured in the Ming times is the culmination of the development of the bureaucratic system in the previous dynasties, gen- erally regarded the epitome of the bureaucratic system in Chinese history (Hucker, 1958). The or- ganization of the system has three tiers in general. The top tier comprises 10 clusters of government offices in the central government, the second has 48 clusters of government agencies, and the third has 230 departments (Zhang et al., 201). Besides abolishing a few top-tier political institutions in the central government to concentrate power in his own hands, the founding emperor of the Ming dynasty (1368-1398). Zhu Yuanzhang, also established new government organs according the social and po- litical needs of the time, forming an organizational structure "largely perpetuated with admiration by the succeeding Ching dynasty (Hucker, 1998)."
#s Hi ((HH)) as these
Given these challenges, the process for making reliable annotations involves a detailed examina- tion of an official's career trajectory, changes their roles, and any evidence of their excrcising military authority. This extensive process includes:
Conducting in-depth reviews and studies of wide range of historical literature.
Verifying evidence by cross-referencing vari- ous historical sources.
D.2
The Interplay of the Political Groups in the Ming Government
Three major political groups operated within the Ming government - civil officials, military offi- cers, and eunuchs. The Ming emperors adopted few measures to prevent one group from superim- posing the others.
Performing rigorous historical analyses.
This procedure requires both expertise in the sub- ject matter and considerable investment of time. For this study, the development of the annota- tion guideline and the annotation of civil officials with military power took us approximately three months.
To limit the power the military officers and in- stitutions, the Ming emperors separated the military leaders from their soldiers by keeping the military

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F Implementation Details for Official
lated to the official. It consisted of normalization layer followed by two projection layers, predicting whether the civil official held military power.
Representation Learning
F.1 Node Initialization
For GNN models, we use learning rate of le-4 for stage 1, and le 3 for stage 2. We set k 20 for constructing the simP-P relation. The models consisted of layers with hidden-layer dimension of 128. Specifically, for HGT and GAT models, we set the number of attention heads to Dropout and carly stopping strategies were employed to prevent overfitting. The specific number of training epochs is given in Table 3.
We encode the texts with the an open-soure, weight of Chinese BERT model (cki) and Hugging-face (Wolf et al., 2020) framework, yielding an initial embedding dimension of 768 for text nodes. We then implemented the metapath2vee model with Py PyTorch-Goometre Geometric (Fey and Lenssen, 2019) to produce a 64 dimension representation with follow- ing settings: walk length at 40, context size at 8, walks per node at 20, number of negative samples at 1, The model was trained for 20 epochs with batch size of 32.
F.3 Handling Class Imbalance
As shown Table 2, our dataset suffered from ex- treme class imbalance. To address this, we utilized an imbalanced sampler implemented in Py PyTrrch- Geometric (Fey and Lenssen, 2019) in both stages of representation learning (see 3.2). In stage I, we upsampled the eunuch and military officer sets to match the number of civil officials. In stage 2, we upsampled the set of civil officials with military power equal the number of civil officials without military power. This sampling process was applied to both our training and validation sets.
G Error Analysis
Govermeent Officess tmpprr Pamily Court Gwvrmment
Cantrul and Capital Viltariss The Ausilurr Captial Narjing
Regionel and Local Goverrance Regional and Local Mill bilryry Units
We conducted an error analysis on the best per- forming GNN model - GAT, and extracted several important findings. Confusion matrices for the pre- diction results under different settings are displayed in Figure 8.
Hicnorary Cvil and Hilitary
Hertor
Figure 10: The official position embeddings learned by the metapath2vec model with a clear clustering aligned with the actual office classification.
The initial official position embeddings, visual- ized in Figure 10, illustrates that official positions within the same offices (points of identical colors) cluster together, signifying that the official position embeddings effectively represents the administra- tive structure of the Ming Dynasty.
617
622
622
0
622
Thue
27
100
18
109
124
121
Predicted
Predicted
Predicted
Predicted
Figure 11: Confusion matrices of the prediction results of GAT on testing data. From left to right: P-O, P-O coP-P, P-O simP-P, P-O coP-P + simP-P.
F.2 Model Implementation and Hyperparameters
We implemented GNN and Multi-layer Perceptron (MLP) models using Torch (Paszke et al., 2019) and
Identification of Nuanced Characteristics, like Military Power Among Civil Officials, is Insuf- ficient with Merely Career Records. The GAT model, despite its strong performance across all settings in predicting Civil Officials without Mili- tary Power (CO w/o MP), incorrectly predicts five false positives when relying only on P-O. These false positives, as exemplified by Zhang Ben (5%
All models use Adam optimizer (Kingma and Ba, 2014), with 31 0.9,
32 0.999, le 08, and batch size 8.
The MLP model, with learning rate to 1e 3, took as input concatenation of the of- ficial's own representation and the averaged rep- resentations the position (office) and/or text re-

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No.
Carcer records
Office Position Category
Reign Period
Hongzhi(3k ( Zhengde( (0)
Right Vice Censor-Fe innsoor-----ee chief in the Censorate (Al Mi 5t)
Other Officers the Censorate H 18% R)
Secretary of the Bureau of Construction
Other Officers in the Ministry in Nanjing (Hi / A
Ministry Works at Nanjing (0i) I. WP m
Chenghua (1826)
AM +: +)
Vice Director of the Bureau of Construction
Other Officers in the Ministry in Nanjing (H) R 1 (B)AA
in the Ministry Works at Nanjing (10
Chenghua (00
[] H3
4
Prefect Xiangyang (HER
Prefectural Officers E)
Chenghua (BZ
5
Prefect Qingzhou (HF Mt (TSI))
Prefectural Officers (lF
Chenghua (bk (t)
Right Administration Vice Commissioner of a Province at Guangdong (llt set Fi (
Core Officers Provincial in the Adminittration Commission (# 4: 18 R.)
Hongzhi (3A
Right Provincial Administration Commis- sioner the Provincial Administration Com- missionat Huguang (3f 0E I8X (
Core Officers Provincial in the Administration Commission (# 12
7
Hongzhi (11
Hongzhi (3 it) Zhengde (iF
8
Grand Coordinator Jiangxi (H (
Grand Coordinator (10
Table Zhang Ben's ' Career Records: A list of positions held by Zhang Ben( with positions often associated with military power, such as Officers Provincial in the Administration Commission (No.6 and 7) and Grand Coordinator (No. 8).
4) in Table 6, depict career trajectories that re- semble those of CO w/ MP. They hold positions often associated with military power, such Grand Coordinator (% M), Supreme Commander (A8 ) Military Superintendent ( ) Minister of War (MM )) Vice Commander-in-Chien H
model consistently misclassified three specific in- dividuals false negatives. These individuals had limited data, with more than two career records and between one and five related texts. Fur- thermore, none of these texts presented direct evi- dence the individuals possessing Military Power (MP). This finding emphasizes the need for abun- dant individual-level information to ensure accu- rate predictions. Therefore, enriching the dataset or leveraging additional data sources might enhance prediction accuracy when the available data is lim- ited.
HO, Officers Provincial in the Administration Commission T and Officers in the Censorate ( We conjec- ture that this similarity leads model to erroneous predictions.
However, the model's performance improved considerably when it incorporated additional person-to-person information, such as co-occurring positions (coP-P), similarity of individual embed- dings (simP-P), both. Consequently, false pos- itives for CO w/o MP were effectively eradicated. This outcome highlights the inadequacy of rely- ing solely on career records to discern nuanced characteristics, such as military power among civil officials.
Direct Evidence of Military Power in Related Narratives Enhances SimP-P Efficacy. Exam- ining the true positive predictions made using offi- cial positions and individual embedding similarity (P-O + simP-P) revealed that related texts often supplied direct evidence of the officials having mil- itary power, especially when other settings (P-O, P-O + coP-P) fell short. An illustrative example includes a narrative stating. "Supervising Secretary Dai Bain ($ and six others who were decreed to be stationed at the border. (#4 + 4F =A a * W confirming Dai Bain (' military power. This evidence substantiates the fectiveness of simP-P in deriving meaningful asso- ciations from textual content, further highlighting the added value of integrating text-based evidence into predictive tasks.
Notably, truc positive predictions using P-O were associated with an average of 23.7 career records, compared to a mere average of 7.5 records for false negatives. This discrepancy underscores the potential value of comprehensive career records in enhancing model accuracy, particularly when the data is confined to career records alone.
Sparse Information on Individuals Inhibits Ac- curate Predictions. Across all four settings, the

---

No.
Career records
Office Position Category
Reign Period
Supervising Secretary in Scrutiny for Revenue
Office of Scrutiny for Revenue (Fi
Jiajing (*9)))
(F Rish (T)
Secretary the Ministry of Justice (f I
Other Officers The Ministry Justice (f
2
+)
#63 tb H)
Jiajing (%)))
Vice Minister in the Scals Office (15
Other Officers in Secretary Offices (#0 # H fi R
3
Jiajing (%14)
Other Officers in Secretary Offices (*0 H 12 A)
4
Vice Minister the Scals Office (1H) xt (i))
Jiajing (%14
Assisant Minister the Court of Imperial En- tertainments( 1 + 25)
5
Court of Imperial Entertainments (1B))
Jiajing (75)))
Vice Goveroor in the Yingtian Superior Pre-
Other Officers in the Yingtian Superior Pre- fecture (Ni X F2 F
6
Jiajing (%
fecture (# ATB
Table Sun Yunzhong's Career Records: record of positions held by Sun Yunzhong (18 f) demonstrating lack of roles typically associated with military power among civil positions.
Abundant Co-occurrence Relations Could Intro- duce Noise. A clear manifestation this issue arises in three instances of false negative predic- tions observed under the settings of P-O coP-P- + simP-P, P-O + coP-P, and P-O alone. Two of these erroneous predictions involve Liu Jingshao ( mii) and Song Ying-Chang ( E). each with fewer than three career records. The third case, Sun Yunzhong( ft. fF), possesses six career records, but significantly deviates from a typical CO w/ MP, as demonstrated in Table 7. The model's incor- rect predictions under the P-O alone setting can be attributed to these atypical career records.
1
A
11
14
1.
15
8
49
4.
A-
However, the P-O coP-P simP-P configu- ration presents different problem, the model still fails to predict correctly despite the availability related texts that provide direct evidence of MP. This issue is exemplified by Song Ying Chang's 's co- occurrence network (Figure 13). Despite military- related narratives present in Song Ying Chang's tex- tual data (Figure 12), an overload of co-occurrence relations introduced by coP-P causes the model to overlook his military power. This underscores the need for future models to employ more refined management or filtering co-occurrence relations, in order minimize noise and improve predictive accuracy.
11
R
16
4
M
11
22
40
Figure 12: Textual Evidence of Military Power: An excerpt from the text providing evidence of military power Song Ying-Chang (R R.). The underlined text in red translates to: 'In previous years, Pacification Commissioner Song Ying-Chang (# N B) was sent on am eastern campaign with more than 70,000 cavalry and infantry soldiers. However, more recently, the total number locally recruited Chinese official soldiers only amounts to 38,000."

---

sett
Song King-Chan
70.9
7C
91
33 13
89 48
A graphical representation of Song King-Chang's (K b) network, depicted in 68 relevant historical texts from Ming Shi Lu. Civil officials, military officers, and non-cate catgoorbge individuals are represented as blue, purple, and pink nodes, respectively. The edge thickness indicates the frequency of co-mentions in historical texts.
Figure 13: Co-occurrence Network of Song

---

Imperial Family and Royal Court
#E Emperors Empresses
The Imperial Clan
Heir Apparent
# Enporr
Emparor and impartal
S Vossa Kings
S2 Easton Paloce
Imparial Court and Inposa Prinpeyy
Importal Harm
EL Eastern Palace
BEI Eunuch Offices
Praceptors Troe Antors
R Dinecornda
P
Banuch Supersbendert Company
Earam Palaee
Figure 14
The Central Government
20 Three Three Solitaries
ABP Six Ministries
S:B Three Soillates
AM MinOtY War
Core Cfiicars in The Minaary
Other Officers in The Ministy
7:0 10 Olher Officens
incuue Three Dukes Three Solfaaria
2B Minider Pencenel
Core Officen in The Mimadty
Other Officers in The Miniaryy
P3
The Grand Secretariat
PM Misster of Revanue
Core chticurs of Roveruu
Officens Minitry of Revorue
'Al offier The Grand Secnataria are idlude in Grand Secnetery"
Mtrider
Ribee
Minittry of Fitne
+ + The Central Secretariat
HBlllt 8033
60 Minsity Juricce
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Core Oftoars in The Minatry
# Other Officra in The Vinsatty Persornal
TB: Minaity Works
Core Offiori The Mineery
Otver Officers in The Minitry
Works
Figure 15
$
Central Administration Assistance
M Secretary Offices
Courts and Directorates
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BHARE Hardin Academy
Bili Harlin Bachelor
DEvOOOEER of
Count the
1838 of
24 Scsieyy
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FM Sexutry
The Soutiyy for
Other Ohtoors in Seoweary Offices
Court of Irgenial
JE Court al Inoeral
Vige-M Acadery
R Dioccornte
The Office of Seaatiy
The Office Sonutiyy for
Figure 16

---

-
Legislation and Censorship
13 H Legislation
Censorahip
8 Cansorat
SE.
BEER BUR tnvesigxiing
1E Pnoddgyino Thiteen Ciroulte
18 Ceniaor
at Consonata
2UB & B Court Judioal Pleview
03:3-10 Grand Canal and Grain Transport
Other Offerss in
Diestor gonarol Gra Transport
Figure 17
Central and Capital Militaries
The Chief Militayy Commissions
Military Training Units Capital
GRAS
S Cormandde-thchef
& S6 Supremi Commandder
INBpANES The Training
S Thaniig
SU50BU Comminnden-s
A Ansiitant Commanden
Offcar
A Commisbbre
(EE Bodypaard
Figure 18
#
The Auxiliary Capital in Nanjing
The Auxiliary Capital in Nanjing
ARAMI Minstar of WaY Nariing
Minider of Fians NaTUPO
Mininae of Tlevatuee
Mixder Pencnnel Nariing
Minider Jusicc Nanarn
R Ministy of Worls Nuniino
Other Officers
Narpin
harsing
M Military Units in Nanjing
A Other Officers in Nanjing
The Legislation and Censorship Nanjing
Millayy Experento Whe henling
Care Offices Miltory Uniss Nonding
MARrAry SupoWRiEnEE Narding
2 Offices Legitsionn and
Olher Offiorsi
Nanting
Figure 19

---

Regional and Local Governance
12
Superior Prefectural Governance
Provincial Governance
E Shuntan Superor
A Shuntar Superir
Oifiwes Provencil
Officers Provinciu The Adriiataaioon
The Admenattaioo
F W Offices the Tingian
Other Officen the Vingtan
(UR Provicial Survellance
Survellance Vice Commisatoont Charge ineedamtt Criuute
1:99 District Governance
# Other Local Officers
1 Prefectural Governance
tEE Sub-prefectural Governance
Dienior Offices
#AA Losser Funcfonatos
Prefectura Offcces
#ill Sub profictaral Officers
Figure 20
Regional and Local Military Units
t6 A "1' Regional Miltary Officials
18.5 A SA Local Miltary Experience
(Auxiliar)) Regional Local Milltayy Units
II Commonddrr
09 Genees-it-Chee
serssisi Ragiona Miltary
Core Raegloed Millay Commissioo
Officrs in Chorge Crrut
BERR
All General
Commissioo
88 I Ragional
WNFT Regional
H Itary
RU Con Officess Commsnnds
M-BEBRFIEE Core ORfians Commissiono
94 Reconde Local
BBS Hegioral Commander
15-F1000 Heeiinhll
DE I Modile Cortoe Commander
3E/1E1 Other Miltary
Figure 21
Military Meritorious Official
Honorary Civil and Military Titles
It t
Military Meritorious Official Titles
" Noble Titles
3 Other Prestige Titles
SU Prestige Civil Titles
Other Merit-based Titles
MMM
MB
XB
t Civil Meritorious Official
PH''" Merit-based Militay Titles
AR Prestige Militry Titles
] Merit-based Civil Titles
Civil Meritorious Official Titles
ZIM
ZBR
SUB
S
Figure 22


Reviews:
[{"review_id": "jHqpofWNOy", "reviewer": "Reviewer_ZgKy", "summary": "", "questions": "1.\tCould the authors better explain the motivation of stage1 training? Moreover, why is only P-O relation used in stage1 but not include other relations? Have the authors ever tried different ways to include the personnel type categorization task, such as joint training with the detection task?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their time, substantial feedback and insightful questions. We also thank the reviewer for recognizing that our paper “can benefit the following research work in this field,” \"offered a guideline to make annotations for other similar tasks\" and \"can be also referred to in other data-driven Chinese historical research studies.\"\n\nWe would like to address the reviewer’s concerns as follows, which will also be added to the revised manuscript:\n\n# Regarding the motivation of and the relation used in stage 1 training:\n\nThe primary motivation behind stage 1 training is to equip the model with a robust understanding of the distinct roles and functionalities of the three major political groups during the Ming dynasty: military officers, civil officials, and eunuchs. The essence of these functionalities is predominantly determined by the official positions held by individuals within these groups. Therefore, the P-O relation, which encapsulates all the positions an individual has held, is a sufficient feature for this stage of training.\n\nDuring our preliminary experiments, we also explored using both P-O and P-P relations in stage 1. However, our findings revealed that integrating the P-P relation in stage 1 led to a slower learning curve for categorization without gain. We also believe that introducing external relations in stage 1 might lead to potential misclassifications --- An illustrative scenario might be a eunuch involved in military tasks being mistakenly categorized as a military officer due to the nature of his activities. Consequently, we chose to solely focus on the P-O relation in stage 1, and in doing so, we set a solid foundation, free from ambiguity, upon which subsequent stages can build.\n\nWe did not try jointly training the personnel categorization task and the detection task, but we believe this could be left as a insightful future follow-up .\n\n# Regarding coP-P and simP-P and their interaction on performance\n\nWe acknowledge and agree with the reviewer's observation regarding the incorporation of coP-P and simP-P and the subsequent negative impact on performance. We also believe that a better method to fuse these two views of P-P interactions would be beneficial.\n\nWe suspect the decreased performance when using both types of relations comes from the long text spans in coP-P relations. Given that these relations are drawn from entire paragraphs, there might be a weak connection between a person mentioned at the beginning and those mentioned at the end of a long paragraph.\n\nWe hypothesize  that using shorter text spans, like sentences, might work better and reduce noise. However, segmenting the paragraphs into sentences is a challenge in our case because the original records, like most Classical Chinese texts,  lack punctuation marks which poses a segmentation challenge, and during our research, we haven't identified a tool that could efficiently address this challenge. We think that future research will look into better ways to combine these P-P interactions, and we'll leave this as a challenge for future study.\n\n\n# Regarding the reliance of one dataset for the experiments and future work\n\nWe thank both reviewer ZgKy and zST9 for pointing out this concern, and we put our response to the valuable concern here.\n\nWe sincerely acknowledge the concerns raised about limiting our experiments to the MingOfficial dataset. Our choice was primarily influenced by the unique nature of the MingOfficial dataset. Through our research, we found no other publicly available datasets that align with MingOfficial's characteristics, which underscores its singular presence in the domain.\n\nOur primary goal is to introduce a new paradigm for addressing similar questions in digital humanities and historical research, emphasizing the unified application of state-of-the-art technical tools in areas previously overlooked. We consider our present effort as an initial demonstration, a proof of concept that paves the way for more extensive research endeavors in the future.\n\nWhile our current approach was tested on a single dataset, it holds potential for wider applications across diverse datasets. To provide more context, our approach is well-suited for probing **historical documents** like Veritable Records of the Joseon Dynasty (朝鮮王朝實錄), Veritable Records of Qing (清實錄), and Chorography (地方誌). Beyond that, we can also delve into **contemporary archives**, especially those that highlight local intricacies and interpersonal dynamics, such as records from Taiwan's Legislature and Hong Kong's Legislative Council. We believe that our current work is just the beginning. It lays the groundwork for wider and more diverse research down the road.\n\nLastly, we appreciate the reviewer's understanding regarding the intricacies of building the MingOfficial dataset. We're proactive in addressing potential biases and omissions in such a domain-specific collection. Collaborating with domain experts has been crucial in ensuring our dataset's comprehensiveness and minimizing any bias.\n\n# Regarding the typos and presentational issues:\n\nWe sincerely thank the reviewer for raising these issues. We will revise the manuscript to correct presentational issues, including typos, grammar errors, missing references and the incomplete line at the end of the limitation section.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThis paper will publish a new dataset containing Ming dynasty officials’ career records, annotated personnel types, and related historical texts, which can benefit the following research work in this field.\n\n2.\tThis work introduced how to process and annotate the proposed dataset, which offered a guideline to make annotations for other similar tasks. Also, the exploratory analysis can help understand this dataset better.\n\n3.\tThe authors provided a graph construction method to present Ming dynasty’s complex political landscape, which can be also referred to in other data-driven Chinese historical research studies.\n\n4.\tThe authors conducted extensive experiments to evaluate the proposed GNN-based framework.\n", "reasons_to_reject": "1.\tThis manuscript needs to be carefully revised in several aspects, including completing the discussion of limitations and addressing various spelling errors (see Typos Grammar Style below).\n\n2.\tFrom the experiment results, it can be found that incorporating coP-P and simP-P always negatively impacted the performance. So, I think there is a need to find a more effective way to fuse these two views of P-P interactions.\n\n3.\tThe experiments were conducted on only one dataset. It would be better if the evaluation of the proposed framework could be performed on more datasets. The scale of the introduced dataset is relatively small, with a more limited number of annotated data available. However, I understand the difficulty to build such an interesting dataset.\n", "questions_to_authors": "1.\tCould the authors better explain the motivation of stage1 training? Moreover, why is only P-O relation used in stage1 but not include other relations? Have the authors ever tried different ways to include the personnel type categorization task, such as joint training with the detection task?", "paper_topic_and_main_contributions": "This paper introduced a new dataset and GNN-based representation learning framework to study the problem of detecting China’s Ming civil officials with military power (CO w/ MP). For the dataset, it consists of career records and personnel types annotations of Ming officials. And in the proposed framework, the authors first constructed a graph with multiple relations among officials, positions and relevant historical texts, and then applied GNN models to encode this information to predict whether an official has military power. Data analysis and experiments are conducted to introduce the dataset and evaluate the proposed framework.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "1.\tLine 154: rotect -> protect\n\n2.\tLine 434: figure11 -> figure8\n\n3.\tLine 436: Appendix Section number\n\n4.\tLimitation: as well as …\n"}, "extra_text": {}}, {"review_id": "4in4T7vcWt", "reviewer": "Reviewer_qxZu", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their time, substantial feedback and insightful questions. We also thank the reviewer for recognizing our paper is on \"an interesting research question\" and its dataset \"could be a useful resource\".\n\nWe would like to address the reviewer’s concerns as follows, which will also be added to the revised manuscript:\n\n# Regarding the Chinese language model used\n\nThe base language model we employed for encoding historical text was not specifically trained on Classical Chinese. At the time of our research, we were unable to identify a well-established model dedicated to Classical Chinese. Our decision to utilize the BERT-base-chinese model as the encoder was informed by our prior experience, where in our separate studies of Ming Dynasty texts found that models tailored for classical Chinese do not show significant improvements in tasks related to text classification that relies heavily on the  embeddings. \n\nWe hypothesize this effectiveness might be due to the similarity between the writing style of the Ming Dynasty historical texts and contemporary Chinese. Additionally, since our task is more on similarity and co-occurrence, we think that a large enough pretrained model such as BERT-base-chinese can produce satisfactory results. \n\nYet, we fully agree that this issue deserves further discussion. We envision that a large language model that can properly be trained / fine-tuned on both Modern and Classical Chinese would benefit study in this kind.\n\n\n# Regarding choice of military power as attributes: \n\nThe motivation behind our choice of military power among civil officials as a special attribute in representation learning is two-fold --- as it's both an important domain-specific problem and a technically valuable challenge.\n\nFirst and foremost, the phenomenon of military power among civil officials is an example of well-studied subjects in historical research yet a challenging one. Accurately identifying attributes like military power among civil officials is both time-consuming and demands specialized domain knowledge, often requiring extensive reading of relevant documentation about the person in question. By focusing on this attribute as an example, we believe our method holds potential in uncovering previously unidentified individuals with this attribute, thereby enriching historical research.\n\nFuthoremore, identifying military power among civil officials is a valuable task from a technical point of view and is a showcase of an efficient method capable of detecting attributes not immediately evident from the career records. Concretely, this task requires that a figure representation should (1) be able to mirror the multifaceted nature of these figures, capturing subtler aspects of their roles and power dynamics, (2) properly handle example with contrastive properties, since holding military power is in stark contrast with civil service associated administrative behaviors. In a nutshell this task presents the models with a rigorous test to showcase the model's learning capability.\n\n# Regarding future work:  \n\nAs we also detailed in the response to Reviewer ZgKy and zST9, although the primary focus of this study centers on a specific dataset, we see our present study as a pioneering step, setting a new paradigm that could be applied to other scenarios which can be future works. The methodology we've developed is well-suited for probing probing **historical documents** like Veritable Records of the Joseon Dynasty (朝鮮王朝實錄), Veritable Records of Qing (清實錄), and Chorography (地方誌) and  **contemporary archives**, especially those that highlight local intricacies and interpersonal dynamics, such as records from Taiwan's Legislature and Hong Kong's Legislative Council. We believe that our work could pave the way for wider and more diverse research down the road.\n\n\n# Regarding the suggestion for an ablation study:\n\nWe agree with the reviewer that exploring the impact of removing or integrating other attributes in the learning objective would be insightful. We earmarked such analyses for upcoming studies  due to space constraints in this paper.\n\n# Regarding the typos and presentational issues:\n\nWe sincerely thank the reviewer for raising these issues. We will revise the manuscript to correct presentational issues, including typos, grammar errors, missing references to Figures and the incomplete line at the end of the limitation section.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "+ representation learning of historical people is an interesting research question\n+ the MingOfficial dataset could be a useful resource", "reasons_to_reject": "- Is the base language model used to encode historical text actually trained on ancient mandarin? How did the authors evaluate this adopted language model?\n\n- Why is military power selected as a special attribute for representation learning? It would be nice to have an ablation study removing this learning objective or adding other attributes as part of the learning signal.\n\n- There is no Figure 11 in the paper, but line 434 indicates otherwise. In addition, there is a ?? on line 436.\n\n- I suggest having an ethics statement to discuss relevant concerns if any. Also, the limitations section is incomplete on line 629.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes MingOfficial, a dataset of official careers in the Ming Dynasty for historical analysis. The dataset contains both structured data and unstructured text for representation learning. Experiments demonstrate that the learned representations with the MingOfficial dataset do demonstrate certain properties of historical significance.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "2d8E8fVYAo", "reviewer": "Reviewer_zST9", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their time, substantial feedback and insightful questions. We also thank the reviewer for recognizing our paper \"addresses a fascinating and essential topic in the field\" and  provides sufficient information to reproduce.\"\n\nWe would like to address the reviewer’s concerns as follows, which will also be added to the revised manuscript:\n\n# Regarding the innovation and the big picture:\n\nWe agree that work indeed leverages largely a wide range of existing techniques, yet we argue that its novelty lies in the unified application of advanced technical tools to a domain not traditionally addressed with such methodologies. We aim to introduce a new paradigm for addressing similar questions in digital humanities and historical research as follows.\n\nConcretely, 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: integrating the rich context from historical texts while minimizing the dependency on labor-intensive interpretation. By leveraging elements that can effectively represent the features of the context, this approach provides a new, efficient avenue for uncovering profound insights into our past.\n\nFurthermore we envision that this new paradigm will fully materialize its potentials when applied to a wide range of historical and modern records/tasks, as we detailed in the paragraph below.\n\n# Regarding the limitation on one dataset and the future works.\n\nWe thank both reviewer ZgKy and zST9 for pointing out this concern, and we put our response to the valuable concern here.\n\nWe sincerely acknowledge the concerns raised about limiting our experiments to the MingOfficial dataset. Our choice was primarily influenced by the unique nature of the MingOfficial dataset. Through our research, we found no other publicly available datasets that align with MingOfficial's characteristics, which underscores its singular presence in the domain.\n\nOur primary goal is to introduce a new paradigm for addressing similar questions in digital humanities and historical research, emphasizing the unified application of state-of-the-art technical tools in areas previously overlooked. We consider our present effort as an initial demonstration, a proof of concept that paves the way for more extensive research endeavors in the future.\n\nWhile our current approach was tested on a single dataset, it holds potential for wider applications across diverse datasets. To provide more context, our approach is well-suited for probing **historical documents** like Veritable Records of the Joseon Dynasty (朝鮮王朝實錄), Veritable Records of Qing (清實錄), and Chorography (地方誌). Beyond that, we can also delve into **contemporary archives**, especially those that highlight local intricacies and interpersonal dynamics, such as records from Taiwan's Legislature and Hong Kong's Legislative Council. We believe that our current work is just the beginning. It lays the groundwork for wider and more diverse research down the road.\n\n\n# Regarding the typos and presentational issues:\n\nWe sincerely thank the reviewer for raising these issues. We will revise the manuscript to correct presentational issues, including typos, grammar errors, missing references to Figures and the incomplete line at the end of the limitation section.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper addresses a fascinating and essential topic in the field of Chinese studies. This topic is of significant interest to scholars and researchers in history and linguistics.\n2. The paper provides sufficient information to reproduce the experimental results.", "reasons_to_reject": "1. The paper's reliance on only one dataset could be perceived as a potential limitation, as it may hinder the ability to establish the universality or general applicability of the proposed method. \n2. The proposed method appears to be primarily built upon existing research, and it lacks sufficient innovation.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper introduces MingOfficial, a multi-modal dataset focusing on officials from China's Ming Dynasty. The authors further propose a GNN-based method to investigate social structures and provides powerful features for downstream tasks. The experiments verify the proposed method can identifying nuanced identities. ", "excitement": 3, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper’s main novel contribution is a new dataset of structured career records and textual historical records for around 10K career officials from the Ming dynasty. They also provide annotations for “civilian officials with military power.” 

One of the challenges of a paper like this is they introduce a novel dataset, novel task (predicting “civilian officials with military power”), and a new method for the new task and dataset (specifically modeling both the structured and unstructured text using graph neural networks). Several reviewers seemed to struggle to unpack these three contributions by noting things like “the experiments were conducted on only one dataset” (Reviewer ZgKy) and “the proposed method appears to be primarily built upon existing research, and it lacks sufficient innovation” (Reviewer zST9). I think these critiques would have been mitigated if the authors had  framed this paper as primarily being a dataset contribution.

If the paper is primarily a dataset contribution paper, the dataset seems novel and important given the bias towards English-only datasets (this is in classical Chinese). As reviewer qxZu hinted at in their comment “Is the base language model used to encode historical text actually trained on ancient mandarin?”, this dataset could potentially be an valuable source for training or evaluating of models for classical Chinese. 

However, I would extend the concern reviewer qxZU had “Why is military power selected as a special attribute for representation learning?” The predictive task seems interesting, although a little esoteric. 

Overall, the dataset contribution is sound and exciting, even if the proposed task (predicting “civilian officials with military power”) and the methods approach with graph neural networks are a bit less exciting.
Decision: Accept-Main