World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
6099
World Ranking
12043
National Ranking
596

Chris Biemann publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Chris Biemann sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 192 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Chris Biemann D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Chris Biemann sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Chris Biemann is affiliated with Universität Hamburg in Germany and has made significant contributions to the field of Computer Science through extensive research and publication.

The main field of study for Chris Biemann is Computer Science, with a strong focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Sociology and Political Science, and Developmental and Educational Psychology. Their work covers a range of topics, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Hate Speech and Cyberbullying Detection
  • Text Readability and Simplification
  • Multi-Agent Systems and Negotiation
  • Advanced Graph Neural Networks

Frequent coauthors in their research endeavors include:

  • Alexander Panchenko
  • Meriem Beloucif
  • Alexander Bondarenko
  • Matthias Hagen
  • Seid Muhie Yimam

Chris Biemann's publications have appeared in various venues. The most frequent publication outlets are arXiv (Cornell University) and Zenodo (CERN European Organization for Nuclear Research), alongside contributions to conferences such as the Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Semantic Web, and the Proceedings of the AAAI Conference on Artificial Intelligence.

Some of the recent notable papers by Chris Biemann include:

  • Neural entity linking: A survey of models based on deep learning, 2022, Semantic Web
  • HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Language Models Explain Word Reading Times Better Than Empirical Predictability, 2022, Frontiers in Artificial Intelligence
  • Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets, 2021, MDPI (MDPI AG)
  • Modern Baselines for SPARQL Semantic Parsing, 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Best Publications

  • What do we need to build explainable AI systems for the medical domain

    Andreas Holzinger;Chris Biemann;Constantinos S. Pattichis;Douglas B. Kell

  • HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

    Binny Mathew;Punyajoy Saha;Seid Muhie Yimam;Chris Biemann

  • Do Supervised Distributional Methods Really Learn Lexical Inference Relations

    Omer Levy;Steffen Remus;Chris Biemann;Ido Dagan

  • Unsupervised Part-of-Speech Tagging Employing Efficient Graph Clustering

    Chris Biemann

  • Ontology Learning from Text: A Survey of Methods

    Unknown

  • Disentangling from babylonian confusion – unsupervised language identification

    Chris Biemann;Sven Teresniak

  • Text: now in 2D! A framework for lexical expansion with contextual similarity

    Unknown

  • The Semantic Web - ISWC 2013

    Harith Alani;Lalana Kagal;Achille Fokoue;Paul Groth

  • Making Sense of Word Embeddings

    Unknown

  • Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings

    Gregor Wiedemann;Steffen Remus;Avi Chawla;Chris Biemann

  • Neural Entity Linking: A Survey of Models Based on Deep Learning

    Özge Sevgili;Artem Shelmanov;Mikhail Y. Arkhipov;Alexander Panchenko

  • A Report on the Complex Word Identification Shared Task 2018

    Seid Muhie Yimam;Chris Biemann;Shervin Malmasi;Gustavo H. Paetzold

  • That’s sick dude!: Automatic identification of word sense change across different timescales

    Unknown

  • An automatic approach to identify word sense changes in text media across timescales

    Unknown

  • Hierarchical Multi-label Classification of Text with Capsule Networks

    Rami Aly;Steffen Remus;Chris Biemann

  • Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno

    Seid Muhie Yimam;Chris Biemann;Richard Eckart de Castilho;Iryna Gurevych

  • IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis

    Ayush Kumar;Sarah Kohail;Amit Kumar;Asif Ekbal

  • Creating a system for lexical substitutions from scratch using crowdsourcing

    Chris Biemann

  • Human and Machine Judgements for Russian Semantic Relatedness

    Alexander Panchenko;Dmitry Ustalov;Nikolay Arefyev;Denis Paperno

  • Text Segmentation with Topic Models

    Unknown

  • Overview of Touché 2020: Argument Retrieval

    Alexander Bondarenko;Maik Fröbe;Meriem Beloucif;Lukas Gienapp

  • UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection

    Gregor Wiedemann;Seid Muhie Yimam;Chris Biemann

  • TARGER: Neural Argument Mining at Your Fingertips

    Artem N. Chernodub;Oleksiy Oliynyk;Philipp Heidenreich;Alexander Bondarenko

  • Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models

    Seid Muhie Yimam;Hizkiel Mitiku Alemayehu;Abinew Ali Ayele;Chris Biemann

  • Overview of Touché 2021: Argument Retrieval

    Alexander Bondarenko;Lukas Gienapp;Maik Fröbe;Meriem Beloucif

  • Graph-based natural language processing and information retrieval rada mihalcea and dragomir radev (university of north texas and university of michigan) cambridge, uk: Cambridge university press, 2011, viii+192 pp; hardbound, isbn 978-0-521-89613-9, $65.00

    Chris Biemann

Frequent Co-Authors

Simone Paolo Ponzetto
Simone Paolo Ponzetto University of Mannheim
Benno Stein
Benno Stein Bauhaus University, Weimar
Henning Wachsmuth
Henning Wachsmuth University of Hannover
Martin Potthast
Martin Potthast Leipzig University
Asif Ekbal
Asif Ekbal Indian Institute of Technology Patna
Ralph Radach
Ralph Radach University of Wuppertal
Pushpak Bhattacharyya
Pushpak Bhattacharyya Indian Institute of Technology Patna
Mona Diab
Mona Diab Carnegie Mellon University
Lucia Specia
Lucia Specia Imperial College London
Arthur M. Jacobs
Arthur M. Jacobs Freie Universität Berlin

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