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Miguel Ballesteros

Miguel Ballesteros

Miguel Ballesteros 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 Miguel Ballesteros 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+

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

Miguel Ballesteros 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 Miguel Ballesteros 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+

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

Overview

Miguel Ballesteros is affiliated with Oracle (US) in the United States. Their research is primarily situated within computer science, with a significant emphasis on artificial intelligence. The scientist's work spans several subfields including information systems, computer networks and communications, as well as computer vision and pattern recognition.

Their scholarly output covers numerous topics linked to natural language processing and text analysis, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Advanced Text Analysis Techniques
  • Text and Document Classification Technologies
  • Speech Recognition and Synthesis
  • Speech and Dialogue Systems
  • Domain Adaptation and Few-Shot Learning

Miguel Ballesteros has contributed to a variety of publication venues. Most frequently, their work appears in:

  • arXiv (Cornell University)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Zenodo (CERN European Organization for Nuclear Research)
  • HAL (Le Centre pour la Communication Scientifique Directe)

Some of their notable recent papers include:

  • Universal Dependencies, 2025, HAL (Le Centre pour la Communication Scientifique Directe)
  • How much pretraining data do language models need to learn syntax?, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Exploring the Role of Task Transferability in Large-Scale Multi-Task Learning, 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations, 2021, Zenodo (CERN European Organization for Nuclear Research)
  • General Purpose Verification for Chain of Thought Prompting, 2024, arXiv (Cornell University)

The scientist regularly collaborates with several co-authors. Frequent collaborators include:

  • Yassine Benajiba
  • Neha Anna John
  • Shuai Wang
  • Yogarshi Vyas
  • Jie Ma

Best Publications

  • Neural Architectures for Named Entity Recognition

    Guillaume Lample;Miguel Ballesteros;Sandeep Subramanian;Kazuya Kawakami

  • Transition-Based Dependency Parsing with Stack Long Short-Term Memory

    Chris Dyer;Miguel Ballesteros;Wang Ling;Austin Matthews

  • Universal Dependencies 2.2

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • Recurrent Neural Network Grammars

    Chris Dyer;Adhiguna Kuncoro;Miguel Ballesteros;Noah A. Smith

  • DyNet: The Dynamic Neural Network Toolkit

    Graham Neubig;Chris Dyer;Yoav Goldberg;Austin Matthews

  • Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs

    Miguel Ballesteros;Chris Dyer;Noah A. Smith

  • Many Languages, One Parser

    Waleed Ammar;George Mulcaire;Miguel Ballesteros;Miguel Ballesteros;Chris Dyer

  • Universal Dependencies 2.1

    Joakim Nivre;Željko Agić;Lars Ahrenberg;Lene Antonsen

  • Universal Dependencies 2.0

    Joakim Nivre;Željko Agić;Lars Ahrenberg;Maria Jesus Aranzabe

  • Universal Dependencies 2.3

    Joakim Nivre;Mitchell Abrams;Željko Agić;Lars Ahrenberg

  • Universal Dependencies 1.2

    Joakim Nivre;Željko Agić;Maria Jesus Aranzabe;Masayuki Asahara

  • Detecting readers with dyslexia using machine learning with eye tracking measures

    Luz Rello;Miguel Ballesteros

  • What Do Recurrent Neural Network Grammars Learn About Syntax

    Adhiguna Kuncoro;Miguel Ballesteros;Lingpeng Kong;Chris Dyer

  • Neural language models as psycholinguistic subjects: Representations of syntactic state.

    Richard Futrell;Ethan Wilcox;Takashi Morita;Peng Qian

  • SemEval 2018 Task 2: multilingual emoji prediction

    Francesco Barbieri;Jose Camacho-Collados;Francesco Ronzano;Luis Espinosa Anke

  • Are Emojis Predictable

    Francesco Barbieri;Miguel Ballesteros;Horacio Saggion

  • Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser

    Adhiguna Kuncoro;Miguel Ballesteros;Lingpeng Kong;Chris Dyer

  • MaltOptimizer: An Optimization Tool for MaltParser

    Miguel Ballesteros;Joakim Nivre

  • Training with Exploration Improves a Greedy Stack LSTM Parser

    Miguel Ballesteros;Yoav Goldberg;Chris Dyer;Noah A. Smith

  • Universal Dependencies 2.7

    Daniel Zeman;Joakim Nivre;Mitchell Abrams;Elia Ackermann

Frequent Co-Authors

Joakim Nivre
Joakim Nivre Uppsala University
Noah A. Smith
Noah A. Smith University of Washington
Christopher D. Manning
Christopher D. Manning Stanford University
Chris Dyer
Chris Dyer Google (United States)
Filip Ginter
Filip Ginter University of Turku
Samuel R. Bowman
Samuel R. Bowman New York University
Sampo Pyysalo
Sampo Pyysalo University of Turku
Jan Hajič
Jan Hajič Charles University
Barbara Plank
Barbara Plank Ludwig-Maximilians-Universität München

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