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Sharifah Mahani Aljunied

Sharifah Mahani Aljunied has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Information extraction, Multilingual & cross-lingual NLP; often with Lidong Bing, Chang Gao, Hwee Tou Ng.

2 papers in our library · 2022–2023

OpenAlex · h-index 7 · 18 works · 149 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 308 times in our library · ⭐ Paper Stars ranking: #5541

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 · China2022–2026 · 7 paperscurrent🏢Alibaba DAMO AcademyHangzhou · China2022–2024 · 4 papers🏢Alibaba GroupGeorge Town · Cayman Islands2022–2024 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 5Research subjects and languages the work is about.Information extraction · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 workVision & language · 1 work

 Works by year

2020s

20231 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022 · 151 citations)
    DocRED is a standard benchmark for document-level relation extraction. It was built by having annotators filter candidate triples suggested by Wikidata distant supervision and an early RE model. The authors show that this process left many true relations unlabeled, so false negatives are widespread. They trace the…
  2. M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models (2023 · 150 citations)
    Most exam-style benchmarks for large language models are English-only, text-only, and mix questions of unclear difficulty.

 Co-authors here

7 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 308 times (of the 149,439 papers whose bibliographies we read); 94 citing papers with a page here →.

Updated

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