Nayeon Lee

12 total papers · 1.0k total citations
6 papers, 403 citations indexed

About

Nayeon Lee is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Nayeon Lee has authored 6 papers receiving a total of 403 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 2 papers in Information Systems and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Nayeon Lee's work include Natural Language Processing Techniques (2 papers), Multimodal Machine Learning Applications (2 papers) and Topic Modeling (2 papers). Nayeon Lee is often cited by papers focused on Natural Language Processing Techniques (2 papers), Multimodal Machine Learning Applications (2 papers) and Topic Modeling (2 papers). Nayeon Lee collaborates with scholars based in South Korea and Hong Kong. Nayeon Lee's co-authors include Tiezheng Yu, Ziwei Ji, Pascale Fung, Bryan Wilie, Samuel Cahyawijaya, Willy Chung, Yan Xu, Dan Su, Holy Lovenia and Wenliang Dai and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and Journal of KIISE.

In The Last Decade

Nayeon Lee

5 papers receiving 390 citations

Hit Papers

A Multitask, Multilingual... 2023 2026 2024 2023 100 200 300

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Nayeon Lee 270 79 60 32 26 6 403
Willy Chung 254 0.9× 79 1.0× 58 1.0× 30 0.9× 25 1.0× 5 384
Holy Lovenia 278 1.0× 79 1.0× 60 1.0× 31 1.0× 33 1.3× 13 418
Julita Bielaniewicz 287 1.1× 120 1.5× 36 0.6× 36 1.1× 22 0.8× 8 443
Stanisław Woźniak 240 0.9× 115 1.5× 31 0.5× 30 0.9× 21 0.8× 5 399
Bartłomiej Koptyra 237 0.9× 114 1.4× 32 0.5× 30 0.9× 21 0.8× 4 386
Konrad Wojtasik 244 0.9× 114 1.4× 31 0.5× 30 0.9× 29 1.1× 3 428
Łukasz Radliński 235 0.9× 114 1.4× 31 0.5× 30 0.9× 20 0.8× 2 383
Oliwier Kaszyca 236 0.9× 118 1.5× 31 0.5× 31 1.0× 21 0.8× 3 388
Igor Cichecki 236 0.9× 118 1.5× 31 0.5× 31 1.0× 21 0.8× 3 388
Mateusz Kochanek 236 0.9× 118 1.5× 31 0.5× 31 1.0× 21 0.8× 3 388

Countries citing papers authored by Nayeon Lee

Since Specialization
Citations

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

Fields of papers citing papers by Nayeon Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nayeon Lee

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

All Works

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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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