Elamur Research⌕
☰
home / authors / Guangwei Xu

Guangwei Xu

Guangwei Xu has 2 papers in our library, in 2021–2022, most of them on Named entity recognition; often with Haitao Zheng, Ning Ding, Pengjun Xie.

2 papers in our library · 2021–2022

OpenAlex · ORCID · h-index 13 · 46 works · 943 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 170 times in our library · ⭐ Paper Stars ranking: #3315

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

🏢Alibaba GroupHangzhou · China2017–2025 · 21 paperscurrent🎓Southern Medical UniversityGuangzhou · China2022–2025 · 4 paperscurrent🏢Alibaba DAMO AcademyHangzhou · China2022 · 3 papers🏢Alibaba GroupGeorge Town · Cayman Islands2022 · 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.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Named entity recognition · 2 works

 Works by year

2020s

20221 paperGuangwei Xu's papers of 202220211 paperGuangwei Xu's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Few-NERD: A Few-shot Named Entity Recognition Dataset (2021 · 335 citations)
    Research on few-shot named entity recognition has lacked a dedicated benchmark. Earlier studies repurposed supervised corpora such as CoNLL'03, WNUT'17 and OntoNotes, which have only 4–18 coarse types and were evaluated under inconsistent settings. Real unseen entities, by contrast, tend to be fine-grained. To fill…
  2. Prompt-learning for Fine-grained Entity Typing (2022 · 180 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:

 Co-authors here

8 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 170 times (of the 141,840 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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