Chung–hye Han

89 total papers · 2.9k total citations
61 papers, 1.2k citations indexed

About

Chung–hye Han is a scholar working on Language and Linguistics, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Chung–hye Han has authored 61 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Language and Linguistics, 36 papers in Artificial Intelligence and 15 papers in Experimental and Cognitive Psychology. Recurrent topics in Chung–hye Han's work include Natural Language Processing Techniques (35 papers), Syntax, Semantics, Linguistic Variation (34 papers) and Language, Discourse, Communication Strategies (15 papers). Chung–hye Han is often cited by papers focused on Natural Language Processing Techniques (35 papers), Syntax, Semantics, Linguistic Variation (34 papers) and Language, Discourse, Communication Strategies (15 papers). Chung–hye Han collaborates with scholars based in Canada, United States and South Korea. Chung–hye Han's co-authors include Maribel Romero, Jeffrey Lidz, Julien Musolino, Peggy Li, Anna Papafragou, Youngon Choi, Martha Palmer, Jong‐Bok Kim, Eon‐Suk Ko and Chungmin Lee and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Cognition and Language.

In The Last Decade

Chung–hye Han

52 papers receiving 929 citations

Author Peers

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

Author Last Decade Papers Cites
Chung–hye Han 836 475 336 182 149 61 1.2k
Susumu Kuno 1.0k 1.2× 585 1.2× 398 1.2× 197 1.1× 156 1.0× 38 1.4k
Laura A. Michaelis 887 1.1× 399 0.8× 427 1.3× 214 1.2× 92 0.6× 55 1.2k
Jean-Pierre Koenig 870 1.0× 445 0.9× 430 1.3× 216 1.2× 164 1.1× 51 1.2k
Jack Hoeksema 1.0k 1.2× 477 1.0× 345 1.0× 302 1.7× 130 0.9× 97 1.3k
Robert D. Levine 808 1.0× 434 0.9× 285 0.8× 261 1.4× 164 1.1× 43 1.2k
Anna Cardinaletti 1.0k 1.2× 433 0.9× 334 1.0× 354 1.9× 243 1.6× 106 1.2k
Masayoshi Shibatani 1.0k 1.2× 454 1.0× 541 1.6× 407 2.2× 144 1.0× 43 1.3k
Klaus von Heusinger 1.1k 1.4× 470 1.0× 491 1.5× 235 1.3× 170 1.1× 71 1.4k
Jonathan Ginzburg 875 1.0× 791 1.7× 431 1.3× 135 0.7× 87 0.6× 67 1.4k
Betty J. Birner 759 0.9× 266 0.6× 312 0.9× 177 1.0× 114 0.8× 37 949

Countries citing papers authored by Chung–hye Han

Since Specialization
Citations

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

Fields of papers citing papers by Chung–hye Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chung–hye Han. 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 Chung–hye Han. The network helps show where Chung–hye Han may publish in the future.

Co-authorship network of co-authors of Chung–hye Han

This figure shows the co-authorship network connecting the top 25 collaborators of Chung–hye Han. A scholar is included among the top collaborators of Chung–hye Han 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 Chung–hye Han. Chung–hye Han 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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