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Peter Brusilovsky

Peter Brusilovsky has 2 papers in our library, in 2017–2026, most of them on Language models & pretraining; often with Daqing He, Rui Meng, Sanqiang Zhao.

2 papers in our library · 2017–2026

OpenAlex · ORCID · Wikidata · h-index 67 · 662 works · 22,600 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 171 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4045

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 PittsburghPittsburgh · United States2000–2026 · 480 papersEmployment since 2000 · Professor, School of Computing and Informationcurrent🎓Carnegie Mellon UniversityPittsburgh · United States1996–2016 · 30 papers🔬All Russian Institute for Scientific and Technical InformationMoscow · Russia1992–1996 · 7 papers🎓Universität TrierTrier · Germany1995–2006 · 4 papers🎓Lomonosov Moscow State UniversityMoscow · Russia1993–2006 · 3 papersEducation🤝Kempelen Institute of Intelligent TechnologiesBratislava · Slovakia2024–2025 · 2 papers🎓University of Technology SydneySydney · Australia1998–2020 · 2 papers🎓Free University of Bozen-BolzanoBolzano · Italy2004–2006 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 11 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).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Science Applications · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20261 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Deep Keyphrase Generation (2017 · 454 citations)
    Keyphrases condense what a document is about. Classical extraction pipelines first gather candidate spans, such as n-grams or noun phrases matching POS patterns, and then rank them with statistical features like TF-IDF or PageRank. These methods can only return phrases that literally occur in the text. Yet in four…
  2. The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance (2026)
    Novice programmers often struggle with compiler errors and failed tests, and instructor feedback does not scale to large courses.

All works

4 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. Deep Keyphrase GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Rui Meng, Sanqiang Zhao, Shuguang Han, et al.2017 · 454 citations
  2. One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetXingdi Yuan et al.2020 · 127 citations
  3. ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustness ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetJan Cegin, Jakub Simko, Peter Brusilovsky2023 · 56 citations
  4. The Effects of Structured LLM-Generated Feedback on Programming Assignment PerformanceWorkshop on Innovative Use of NLP for Building Educational Applications (BEA)Tsvetomila Mihaylova, Evanfiya Logacheva, Arto Hellas, et al.2026

 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 171 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

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