World's Best Scientists 2026 revealed!

Ruifeng Xu 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 Ruifeng Xu 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.

Ruifeng Xu 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 Ruifeng Xu 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

Ruifeng Xu is affiliated with the Harbin Institute of Technology in China. Their primary field of research is Computer Science, with a substantial focus on Artificial Intelligence. Additionally, their work covers subfields such as Computer Vision and Pattern Recognition, Management Science and Operations Research, Media Technology, and Information Systems.

The researcher's work addresses various topics in computational methods and machine learning, including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Sentiment Analysis and Opinion Mining
  • Advanced Text Analysis Techniques
  • Multimodal Machine Learning Applications
  • Speech and Dialogue Systems
  • Text and Document Classification Technologies

Notable recent publications by Ruifeng Xu include:

  • "Exploring Privileged Features for Relation Extraction with Contrastive Student-Teacher Learning," 2022, IEEE Transactions on Knowledge and Data Engineering
  • "Multi-goal multi-agent learning for task-oriented dialogue with bidirectional teacher-student learning," 2020, Knowledge-Based Systems
  • "An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing," 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "Dual Pseudo Supervision for Semi-Supervised Text Classification with a Reliable Teacher," 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • "A Quantum Expectation Value Based Language Model with Application to Question Answering," 2020, Entropy

Frequent co-authors collaborating with Ruifeng Xu include:

  • Chengming Li
  • Min Yang
  • Jianzhu Bao
  • Yang Sun

The scientist has published extensively in several venues with the highest number of contributions appearing in arXiv (Cornell University) and Zenodo (CERN European Organization for Nuclear Research). Other venues include IEEE Transactions on Knowledge and Data Engineering, Knowledge-Based Systems, and the Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.

Best Publications

  • Aleph: a detector for electron-positron annihilations at Lep

    D Decamp;B Deschizeaux;Jp Lees;Mn Minard

  • Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

    Tao Chen;Ruifeng Xu;Yulan He;Xuan Wang

  • Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks

    Bin Liang;Hang Su;Lin Gui;Erik Cambria

  • A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis

    Qingnan Jiang;Lei Chen;Ruifeng Xu;Xiang Ao

  • Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection

    Bin Liu;Deyuan Zhang;Ruifeng Xu;Jinghao Xu

  • A Question Answering Approach for Emotion Cause Extraction

    Lin Gui;Jiannan Hu;Yulan He;Ruifeng Xu

  • Stance classification with target-specific neural attention networks

    Jiachen Du;Ruifeng Xu;Yulan He;Lin Gui

  • Event-driven emotion cause extraction with corpus construction

    Lin Gui;Dongyin Wu;Ruifeng Xu;Qin Lu

  • PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation.

    Bin Liu;Jinghao Xu;Shixi Fan;Ruifeng Xu

  • Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis

    Tao Chen;Ruifeng Xu;Yulan He;Yunqing Xia

  • Disease named entity recognition by combining conditional random fields and bidirectional recurrent neural networks

    Qikang Wei;Tao Chen;Ruifeng Xu;Yulan He

  • Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction

    Chuang Fan;Chaofa Yuan;Jiachen Du;Lin Gui

  • Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

    Ruifeng Xu;Jiyun Zhou;Hongpeng Wang;Yulan He

  • Emotion Cause Detection with Linguistic Construction in Chinese Weibo Text

    Lin Gui;Li Yuan;Ruifeng Xu;Bin Liu

  • Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis

    Bin Liang;Rongdi Yin;Lin Gui;Jiachen Du

  • Introduction to Chinese Natural Language Processing

    Kam-Fai Wong;Wenji Li;Ruifeng Xu;Zheng-sheng Zhang

  • Learning representations from heterogeneous network for sentiment classification of product reviews

    Lin Gui;Yu Zhou;Ruifeng Xu;Yulan He

  • Using distances between Top-n-gram and residue pairs for protein remote homology detection.

    Bin Liu;Bin Liu;Jinghao Xu;Quan-Ming Zou;Ruifeng Xu

  • Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

    Ruifeng Xu;Jiyun Zhou;Bin Liu;Yulan He

  • Word Embedding Composition for Data Imbalances in Sentiment and Emotion Classification

    Ruifeng Xu;Tao Chen;Yunqing Xia;Qin Lu

Frequent Co-Authors

Qin Lu
Qin Lu Hong Kong Polytechnic University
Yulan He
Yulan He King's College London
Xiaolong Wang
Xiaolong Wang University of California, San Diego
Bin Liu
Bin Liu National University of Singapore
Kam-Fai Wong
Kam-Fai Wong Chinese University of Hong Kong
Wenjie Li
Wenjie Li Hong Kong Polytechnic University
Lidong Bing
Lidong Bing Carnegie Mellon University
Ying Shen
Ying Shen Sun Yat-sen University
Shuming Shi
Shuming Shi Tencent (China)
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute

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