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Hong-Gee Kim

Hong-Gee Kim has 2 papers in our library, in 2022–2026, most of them on Named entity recognition; often with Guangwei Xu, Hai-Tao Zheng, Haitao Zheng.

2 papers in our library · 2022–2026

OpenAlex · ORCID · Wikidata · h-index 30 · 252 works · 2,826 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 55 times in our library · ⭐ Paper Stars ranking: #5791

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Seoul National UniversitySeoul · South Korea2004–2026 · 174 paperscurrent🏥Seoul National University Dental HospitalSeoul · South Korea2014–2025 · 6 papers🎓Ollscoil na Gaillimhe – University of GalwayGalway · Ireland2007–2010 · 5 papers🎓Dankook UniversityJukjeon-dong · South Korea2002–2010 · 4 papers🏢Pohang Iron and SteelPohang · South Korea2021–2022 · 3 papers🏢Samsung ElectronicsSeoul · South Korea2010–2011 · 3 papers🎓National University of IrelandDublin · Ireland2010–2011 · 2 papers🏢SamsungSeoul · South Korea2010 · 2 papers🎓University of GeorgiaAthens · United States1997–2002 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Named entity recognition · 1 work

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Prompt-learning for Fine-grained Entity Typing (2022 · 181 citations)
    The paper asks whether prompt-learning, which recasts a downstream task as a cloze-style masked-word prediction, can replace classifier-based fine-tuning for fine-grained entity typing. It focuses on cases with little or no labelled data. The authors build a simple pipeline called PLET with two parts:
  2. GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026 · 8 citations)
    Soft prompt compression shrinks long LLM inputs into a few learned vectors. The authors identify two weaknesses in existing methods. First, appended summary tokens tend to be dominated by a few anchor tokens during autoencoder pretraining. Second, high-level summary vectors are fed to the decoder as though they were…

 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 55 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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