Elamur Research⌕
☰
home / authors / Zifei Shan

Zifei Shan

Zifei Shan has 2 papers in our library, in 2020–2026, most of them on Named entity recognition, Multilingual & cross-lingual NLP; often with Dacheng Tao, Daniel Gillick, Jan A. Botha.

2 papers in our library · 2020–2026

OpenAlex · ORCID · Wikidata · h-index 8 · 35 works · 243 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 46 times in our library · ⭐ Paper Stars ranking: #6643

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

🏢TencentShenzhen · China2023–2026 · 18 paperscurrent🎓Peking UniversityBeijing · China2012–2015 · 4 papers🏢GoogleMountain View · United States2020 · 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).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 1 workNamed entity recognition · 1 work

 Works by year

2020s

20261 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Entity Linking in 100 Languages (2020 · 114 citations)
    Most entity linking (EL) research beyond monolingual settings is cross-lingual. It assumes one KB language, usually English Wikipedia, and so ignores the many entities described only in other languages. Botha, Shan and Gillick instead propose Multilingual Entity Linking (MEL). In MEL, mentions in any language are…
  2. RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026)
    Mixture-of-Agents (MoA) setups let several LLMs refine each other's answers over multiple layers. Because every model runs at every layer, this is expensive and slow. Sparse MoA adds an LLM judge to filter answers, but all models still have to generate before the judge sees them. Neither approach scales well to pools…

 Co-authors here

5 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 46 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/zifei-shan/