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Nakanyseth Vuth

Nakanyseth Vuth has 2 papers in our library, in 2026, most of them on Named entity recognition, Information extraction, Low-resource languages; often with Didier Schwab, Gilles Sérasset, Benjamin Lecouteux.

2 papers in our library · 2026

OpenAlex · h-index 2 · 11 works · 4 citations OpenAlex, 2026-10-14

📄 2 works in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #12155

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

🔬Laboratoire d'Informatique de GrenobleGrenoble · France2023–2026 · 9 paperscurrent🎓Université Grenoble AlpesSaint-Martin-d'Hères · France2024–2026 · 8 paperscurrent🔬Centre National de la Recherche ScientifiqueParis · France2024–2026 · 5 paperscurrent🎓Institut polytechnique de GrenobleGrenoble · France2024–2026 · 4 paperscurrent

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 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 4Research subjects and languages the work is about.Information extraction · 1 workKnowledge graphs & ontologies · 1 workLow-resource languages · 1 workNamed entity recognition · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. From Latents to Labels: Zero-Shot Named Entity Recognition using Sparse Autoencoder Features (2026)
    Zero-shot NER is usually done by prompting LLMs, which is costly, sensitive to wording and hard to make consistent, or by probing dense hidden states, where polysemantic neurons make supervision necessary.
  2. Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage Domain (2026)
    Building NER and RE systems for niche fields is hard because expert-labelled data is scarce. Existing Reverse-IE methods create synthetic training text from knowledge graphs, but they assume such a knowledge base already exists. The authors drop that assumption and start from a formal ontology instead. Their pipeline…

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. From Latents to Labels: Zero-Shot Named Entity Recognition using Sparse Autoencoder FeaturesJoint Conference on Lexical and Computational Semantics (*SEM)Nakanyseth Vuth, Gilles Sérasset, Didier Schwab2026
  2. Ontology-Guided Synthetic Data Generation for Low-Resource Information Extraction: A Case Study in IT Heritage DomainKnowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26Nakanyseth Vuth, Emrick Poncet, Gilles Sérasset, et al.2026
  3. “POPCORN-RENS : un nouveau jeu de données en français annoté en entités d'intérêts sur une thématique ““sécurité et défense””” ↗Actes de l'atelier Évaluation des modèles génératifs (LLM) et challenge 2025 (EvalLLM) · no summary yetLucas Aubertin et al.2025

 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.

Updated

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