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Shu-Kai Hsieh

Shu-Kai Hsieh has 2 papers in our library, in 2026, most of them on Word sense disambiguation, Language models & pretraining, Idioms & multiword expressions; often with Lang-Ching Yeh, Yu-Chieh Wang.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 11 · 139 works · 425 citations OpenAlex, 2026-10-14

📄 2 works in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #13911

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

🎓National Taiwan UniversityTaipei · Taiwan2010–2026 · 59 paperscurrent🎓National Taiwan Normal UniversityTaipei · Taiwan2007–2019 · 17 papers🔬Academia SinicaTaipei · Taiwan2006–2018 · 6 papers🎓National Ilan UniversityYilan · Taiwan2007–2008 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksLiterature and Literary Theory · 1 work

🧭 Topics 3Research subjects and languages the work is about.Idioms & multiword expressions · 1 workLanguage models & pretraining · 1 workWord sense disambiguation · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Capturing Ancient Chinese Sense Induction with Automatic Pipelines (2026)
    Computational resources that trace how word meanings evolve are scarce for Ancient Chinese, a gap this paper addresses.
  2. Lexical Familiarity Predicts Processing Depth for Nonliteral Language in Large Language Models (2026)
    The paper asks whether large language models handle different kinds of nonliteral language at different depths, and what principle governs this.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Capturing Ancient Chinese Sense Induction with Automatic PipelinesWorkshop on Language Technologies for Historical and Ancient Languages @ LRECGuan-Yu Tseng, Chunki Lim, Chih-Han Lin, et al.2026
  2. Lexical Familiarity Predicts Processing Depth for Nonliteral Language in Large Language ModelsWorkshop on Trustworthy NLPLang-Ching Yeh, Yu-Chieh Wang, Shu-Kai Hsieh2026
  3. Towards an Automatic Measurement of Verbal Lexicon Acquisition: The Case for a Young Children-versus-Adults Classification in French and Mandarin ↗Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation · no summary yetYann Desalle et al.2010

 Co-authors here

2 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.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/shu-kai-hsieh/