🧑‍🔬 David J. Newman

🏛️ Affiliations

🏛️National Cancer InstituteBethesda · United States1995–2025 · 123 paperscurrent🏛️National Institutes of HealthBethesda · United States2001–2025 · 45 paperscurrent🎓Loma Linda UniversityLoma Linda · United States2025 · 1 papercurrent🔬Frederick National Laboratory for Cancer ResearchFrederick · United States1995–2021 · 39 papers🎓University of California, IrvineIrvine · United States2003–2013 · 30 papers🔬South West Thames Institute for Renal ResearchLondon · United Kingdom1998–2006 · 21 papers🎓Queen Mary University of LondonLondon · United Kingdom1961–2015 · 20 papers🏥St Helier HospitalLondon · United Kingdom1998–2004 · 18 papers🔬Data61Sydney · Australia2009–2011 · 8 papers🎓North Dakota State UniversityFargo · United States2011–2014 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 52 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.

📅 Works by year

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Automatic Evaluation of Topic Coherence (2010 · 826 citations)
   Topic models such as LDA produce lists of words that are meant to represent themes. These models are usually judged by perplexity, which says little about whether people find a topic meaningful. Newman et al. define a new task: automatically scoring a topic's coherence, meaning how easily a human could interpret it…
2. Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality (2014 · 704 citations)
   Topic models such as LDA are often shown directly to people, so the quality that matters is whether their topics make sense to humans.
3. Word Sense Induction for Novel Sense Detection (2012 · 187 citations)
   Lau et al. tackle word sense induction (WSI), the task of discovering a word's senses from unlabelled usage rather than from a fixed inventory. They also show that WSI can serve a practical lexicographic purpose: detecting words that have acquired new senses. Each target word gets its own topic model. Every context…

All works

10 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Automatic Evaluation of Topic CoherenceConference of the North American Chapter of the Association for Computational Linguistics (NAACL)David Newman, Jey Han Lau, Karl Grieser, et al.2010 · 826 citations
2. Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model QualityConference of the European Chapter of the Association for Computational Linguistics (EACL)Jey Han Lau, David Newman, Timothy Baldwin2014 · 704 citations
3. Fast collapsed gibbs sampling for latent dirichlet allocation ↗ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · no summary yetIan Porteous et al.2008 · 578 citations
4. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation ↗no summary yetYee Whye Teh, David J. Newman, Max Welling2007 · 504 citations
5. Distributed Algorithms for Topic Models ↗no summary yetDavid J. Newman et al.2009 · 399 citations
6. Distributed Inference for Latent Dirichlet Allocation ↗no summary yetDavid J. Newman et al.2007 · 214 citations
7. Word Sense Induction for Novel Sense DetectionConference of the European Chapter of the Association for Computational Linguistics (EACL)Jey Han Lau, Paul Cook, Diana McCarthy, et al.2012 · 187 citations
8. Jordan Boyd-Graber, David Mimno, and David Newman. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. Handbook of Mixed Membership Models and Their Applications, 2014. ↗no summary yetBoca Raton et al.2014 · 10 citations
9. Distributed Gibbs Sampling for Latent Variable Models ↗Cambridge University Press eBooks · no summary yetArthur Asuncion et al.2011 · 5 citations
10. Machine Learning on Very Large Data Sets: Distributed Gibbs Sampling for Latent Variable Models ↗no summary yetDavid J. Newman et al.2012 · 1 citation

🤝 Co-authors here

4 co-authors with a page, the most papers together first.

• Jey Han Lau ×3
• Timothy Baldwin ×3
• Diana McCarthy
• Paul Cook

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference of the European Chapter of the Association for Computational Linguistics (EACL) ×2: EACL 2014, EACL 2012
• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2010
• ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
• Cambridge University Press eBooks

🔗 Cited by

Papers of our library cite their works 388 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated 30 October 2026

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