Tingchen Fu

414 citations
8 papers · 67 indexed · 1 hit paper · h-index 4
Topics
Topic Modeling (6 papers)Natural Language Processing Techniques (5 papers)Speech and dialogue systems (5 papers)
Journals
SHILAP Revista de lepidopterologíaComputational LinguisticsProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Partner nations
ChinaFinlandHong Kong

In The Last Decade

Tingchen Fu

6 papers receiving 64 citations

Hit Papers

🧜Siren’s Song in the AI Ocean: A Survey on Hallucination...202520262025510152025

Peers

Tingchen Fu
Comparison fields: 5 of 30
  • Artificial Intelligence 45
  • Computer Vision and Pattern Recognition 6
  • Health Informatics 5
  • Computer Science Applications 5
  • Developmental and Educational Psychology 5
Replace Fereshte Khani with:
Fereshte Khani United States
J. Heu South Korea
Sahib Singh United States
Dumisizwe Bhembe United States
Bjarke Felbo United States
Marco Antonio Sobrevilla Cabezudo Brazil
Damien Sileo France
Rafael Rafailov United States
Tomasz Szandała Poland
Rachel Sterneck United States
Tingchen Fu relative to Fereshte Khani United States Fereshte Khani's profile →
Citations per field
00.5×
Fereshte Khani · 1×
Citations per year

Countries citing papers authored by Tingchen Fu

Since Specialization
Citations

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

Fields of papers citing papers by Tingchen Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tingchen Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Tingchen Fu. A scholar is included among the top collaborators of Tingchen Fu 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 Tingchen Fu. Tingchen Fu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
#WorkIndexed citations
1
🧜Siren’s Song in the AI Ocean: A Survey on Hallucination in Large Language Modelsbreakdown →
27
2 1
3 1
4 24
5 0
6 2
7 9
8 3

About Tingchen Fu

Tingchen Fu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 8 papers that have together received 67 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers) and Speech and dialogue systems (5 papers). The work is most often cited by research in Health Informatics (5 citations), Artificial Intelligence (45 citations) and Computer Science Applications (5 citations). Tingchen Fu has collaborated with scholars based in China, Finland and Hong Kong. Frequent co-authors include Rui Yan, Ji-Rong Wen, Xueliang Zhao, Shen Gao, S. Shi, Yafu Li, Yanwen Zhang, Yulong Chen, Wei Bi and Chongyang Tao. Their work appears in journals such as SHILAP Revista de lepidopterología, Computational Linguistics and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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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