Chaojun Xiao

1.7k citations
22 papers · 551 indexed · 2 hit papers · h-index 8
Topics
Topic Modeling (14 papers)Natural Language Processing Techniques (13 papers)Recommender Systems and Techniques (5 papers)
Journals
SHILAP Revista de lepidopterologíaACM Transactions on Information SystemsNature Machine Intelligence

In The Last Decade

Chaojun Xiao

21 papers receiving 538 citations

Hit Papers

Legal Judgment Prediction via Topological Learning20182026202020232018202150100150

Peers

Chaojun Xiao
Comparison fields: 5 of 57
  • Artificial Intelligence 408
  • Political Science and International Relations 318
  • Law 134
  • Economics and Econometrics 85
  • Information Systems 58
Replace Yunqiu Shao with:
Yunqiu Shao China
Shuzi Niu China
Stefanie Brüninghaus United States
Jack G. Conrad United States
Prodromos Malakasiotis Greece
E. L. Rissland United States
Erich Schweighofer Austria
Zhunchen Luo China
Peter Hammond United Kingdom
Juliano Rabelo Canada
Chaojun Xiao relative to Yunqiu Shao China Yunqiu Shao's profile →
Citations per field
00.5×7.7×
Yunqiu Shao · 1×
Citations per year

Countries citing papers authored by Chaojun Xiao

Since Specialization
Citations

This map shows the geographic impact of Chaojun Xiao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chaojun Xiao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chaojun Xiao more than expected).

Fields of papers citing papers by Chaojun Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chaojun Xiao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chaojun Xiao. The network helps show where Chaojun Xiao may publish in the future.

Co-authorship network of co-authors of Chaojun Xiao

This figure shows the co-authorship network connecting the top 25 collaborators of Chaojun Xiao. A scholar is included among the top collaborators of Chaojun Xiao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chaojun Xiao. Chaojun Xiao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 1
3 3
4 0
5 1
6 4
7 7
8 2
9 1
10 1
11 23
12
Lawformer: A pre-trained language model for Chinese legal long documentsbreakdown →
131
13 43
14 2
15 33
16 21
17 1
18 59
19 27
20
Legal Judgment Prediction via Topological Learningbreakdown →
186

About Chaojun Xiao

Chaojun Xiao is a scholar working on Artificial Intelligence, General Social Sciences and Information Systems, having authored 22 papers that have together received 551 indexed citations. Recurring topics across this work include Topic Modeling (14 papers), Natural Language Processing Techniques (13 papers) and Recommender Systems and Techniques (5 papers). The work is most often cited by research in Law (134 citations), Political Science and International Relations (318 citations) and Artificial Intelligence (408 citations). Chaojun Xiao has collaborated with scholars based in China, United Kingdom and Singapore. Frequent co-authors include Maosong Sun, Zhiyuan Liu, Cunchao Tu, Haoxi Zhong, Zhipeng Guo, Xu Han, Tianyang Zhang, Yankai Lin, Ruobing Xie and Yuan Yao. Their work appears in journals such as SHILAP Revista de lepidopterología, ACM Transactions on Information Systems and Nature Machine Intelligence.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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