Elamur Research⌕
☰
home / authors / Sha Li

Sha Li

Sha Li has 2 papers in our library, in 2021–2023, most of them on Code & programming languages; often with Heng Ji, Jiawei Han, Xingyao Wang.

2 papers in our library · 2021–2023

OpenAlex · ORCID · h-index 5 · 15 works · 203 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 244 times in our library · ⭐ Paper Stars ranking: #3171

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

🎓University of Illinois Urbana-ChampaignUrbana · United States2022–2023 · 4 paperscurrent🎓Shandong Vocational and Technical University of International StudiesRizhao · China2022 · 2 papers🎓University of YorkYork · United Kingdom2016–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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.Code & programming languages · 1 work

 Works by year

2020s

20231 paperSha Li's papers of 202320211 paperSha Li's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Document-Level Event Argument Extraction by Conditional Generation (2021 · 432 citations)
    Most event extraction systems work within single sentences. In real documents, however, an event's participants are often mentioned in neighbouring sentences or only as pronouns, so sentence-level extraction gives incomplete or uninformative results. Li, Ji and Han reframe document-level argument extraction as…
  2. Code4Struct: Code Generation for Few-Shot Event Structure Prediction (2023 · 128 citations)
    CODE4STRUCT tests whether large language models trained on both code and natural language can handle structured prediction when the target structure is written as a program.

 Co-authors here

3 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 244 times (of the 141,840 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

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