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Martin Theobald

Martin Theobald has 2 papers in our library, in 2016–2026, most of them on Named entity recognition, Information retrieval & search; often with Gerhard Weikum, Salima Lamsiyah.

2 papers in our library · 2016–2026

OpenAlex · ORCID · h-index 26 · 216 works · 3,407 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 12 times in our library · ⭐ Paper Stars ranking: #7231

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 LuxembourgLuxembourg · Luxembourg2016–2026 · 61 paperscurrent🔬Max Planck Institute for InformaticsSaarbrücken · Germany2002–2014 · 70 papers🔬Max Planck SocietyMunich · Germany2002–2014 · 48 papers🎓Stanford UniversityStanford · United States2007–2018 · 13 papers🎓University of AntwerpAntwerp · Belgium2013–2014 · 9 papers🎓Universität UlmUlm · Germany2013–2018 · 7 papers🎓Saarland UniversitySaarbrücken · Germany2003 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workMolecular Biology · 1 workInformation Systems · 1 work

🧭 Topics 2Research subjects and languages the work is about.Named entity recognition · 1 workInformation retrieval & search · 1 work

 Works by year

2020s

20261 paper

2010s

20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. J-NERD: Joint Named Entity Recognition and Disambiguation with Rich Linguistic Features (2016 · 75 citations)
    Typical entity-linking pipelines first detect and type name mentions (NER), then link them to a knowledge base (NED).
  2. RAGVUE: A Diagnostic View for Explainable and Automated Evaluation of Retrieval-Augmented Generation (2026 · 1 citation)
    Assessing retrieval-augmented generation (RAG) pipelines is difficult because a bad answer may come from poor retrieval, weak generation, or claims not backed by the retrieved evidence.

 Co-authors here

2 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 12 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

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