Mosha Chen

1.2k citations
19 papers · 543 · h-index 13

Impact in

Papers in

    • Topic Modeling 19
    • Natural Language Processing Techniques 13
    • Advanced Graph Neural Networks 3
    • Advanced Text Analysis Techniques 2
    • Sentiment Analysis and Opinion Mining 2
    • Biomedical Text Mining and Ontologies 5

Mosha Chen

18 papers receiving 533 citations

Peers

Mosha Chen
Comparison fields: 5 of 53
  • Artificial Intelligence 509
  • Management Science and Operations Research 76
  • Health Informatics 6
  • Information Systems 67
  • Computer Vision and Pattern Recognition 47
Replace Yuning Mao with:
Yuning Mao United States
Kun Xu China
Congle Zhang United States
Minghao Hu China
Oier López de Lacalle Spain
Shyam Upadhyay United States
Yunzhi Yao China
Thomas Lin United States
Luciano Del Corro Germany
Arzoo Katiyar United States
Mosha Chen relative to Yuning Mao United States Yuning Mao's profile →
Citations per field
00.5×3.6×
Yuning Mao · 1×
Citations per year

Countries citing papers authored by Mosha Chen

Since Specialization
Citations

This map shows the geographic impact of Mosha Chen'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 Mosha Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mosha Chen more than expected).

Fields of papers citing papers by Mosha Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mosha Chen. 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 Mosha Chen. The network helps show where Mosha Chen may publish in the future.

Co-authors

The 25 scholars most cited alongside Mosha Chen, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mosha Chen Line = papers co-authored together Mosha Chen links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2021102
2 202072
3 202170
4 202261
5 202249
6 202133
7 202132
8 202126
9 202123
10 202017
11 201016
12 202112
13 202312
14 20186
15 20215
16 20204
17 20202
18 20221
19 20230

About Mosha Chen

Mosha Chen is a scholar working on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Management Science and Operations Research and Pulmonary and Respiratory Medicine, having authored 19 papers that have together received 543 indexed citations. Recurring topics across this work include Topic Modeling (19 papers), Natural Language Processing Techniques (13 papers), Biomedical Text Mining and Ontologies (5 papers), Advanced Graph Neural Networks (3 papers), Advanced Text Analysis Techniques (2 papers), Multimodal Machine Learning Applications (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Data Quality and Management (2 papers). The work is most often cited by research in Artificial Intelligence (509 citations), Management Science and Operations Research (76 citations), Health Informatics (6 citations), Information Systems (67 citations) and Computer Vision and Pattern Recognition (47 citations). Mosha Chen has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Chuanqi Tan, Fei Huang, Ningyu Zhang, Huajun Chen, Shumin Deng, Songfang Huang, Xin Xie, Wei Qiu, Rui Wang and Yao Fu. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, ACM Computing Surveys, JMIR Medical Informatics, Proceedings of the AAAI Conference on Artificial Intelligence and arXiv (Cornell University).

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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