World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
34408
World Ranking
5191
National Ranking
2382

Overview

Salim Roukos is affiliated with IBM in the United States and has made contributions primarily to the field of Computer Science, with a particular focus on Artificial Intelligence. Their work spans various subfields including Computer Vision and Pattern Recognition, Molecular Biology, Electrical and Electronic Engineering, and Management Science and Operations Research.

The scientist's research covers several key topics, most notably:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Multimodal Machine Learning Applications
  • Semantic Web and Ontologies
  • Text Readability and Simplification
  • Machine Learning in Bioinformatics
  • Advanced Graph Neural Networks

Salim Roukos has contributed to a number of publications appearing in various academic venues, with frequent outputs in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Transactions of the Association for Computational Linguistics

Recent scholarly works include:

  • "Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing" (2021), published in the Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • "A Semantic Parsing and Reasoning-Based Approach to Knowledge Base Question Answering" (2021), featured in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training" (2020), available on arXiv
  • "Maximum Bayes Smatch Ensemble Distillation for AMR Parsing" (2022), published in the Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • "Multi-Stage Pre-training for Low-Resource Domain Adaptation" (2020), also on arXiv

Collaborative efforts form an important part of their research profile, with frequent co-authors including:

  • Radu Florian
  • Ramón Fernández Astudillo
  • Tahira Naseem
  • Young-Suk Lee
  • Pavan Kapanipathi

Best Publications

  • Bleu: a Method for Automatic Evaluation of Machine Translation

    Kishore Papineni;Salim Roukos;Todd Ward;Wei-Jing Zhu

  • Procedure for quantitatively comparing the syntactic coverage of English grammars

    S. Abney;S. Flickenger;C. Gdaniec;C. Grishman

  • Natural language task-oriented dialog manager and method

    Kishore A. Papineni;Salim Roukos;Robert T. Ward

  • Continuous hidden Markov modeling for speaker-independent word spotting

    J.R. Rohlicek;W. Russell;S. Roukos;H. Gish

  • A Statistical Model for Multilingual Entity Detection and Tracking

    R. Florian;H. Hassan;A. Ittycheriah;H. Jing

  • Phrase splicing and variable substitution using a trainable speech synthesizer

    Robert E. Donovan;Martin Franz;Salim E. Roukos;Jeffrey Sorensen

  • A stochastic segment model for phoneme-based continuous speech recognition

    M. Ostendorf;S. Roukos

  • A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree

    Xiaoqiang Luo;Abe Ittycheriah;Hongyan Jing;Nanda Kambhatla

  • Statistical natural language understanding using hidden clumpings

    M. Epstein;K. Papineni;S. Roukos;T. Ward

  • A maximum entropy model for prepositional phrase attachment

    Adwait Ratnaparkhi;Jeff Reynar;Salim Roukos

  • Challenges in information retrieval and language modeling: report of a workshop held at the center for intelligent information retrieval, University of Massachusetts Amherst, September 2002

    James Allan;Jay Aslam;Nicholas Belkin;Chris Buckley

  • Trigger-based language models: a maximum entropy approach

    R. Lau;R. Rosenfeld;S. Roukos

  • Statistical translation system with features based on phrases or groups of words

    Kishore Ananda Papineni;Salim Estephan Roukos;Robert Todd Ward

  • Active Learning for Statistical Natural Language Parsing

    Min Tang;Xiaoqiang Luo;Salim Roukos

  • A dynamic language model for speech recognition

    F. Jelinek;B. Merialdo;S. Roukos;M. Strauss

  • Performance of the IBM large vocabulary continuous speech recognition system on the ARPA Wall Street Journal task

    L.R. Bahl;S. Balakrishnan-Aiyer;J.R. Bellgarda;M. Franz

  • Building scalable N-gram language models using maximum likelihood maximum entropy N-gram models

    Raymond Lau;Ronald Rosenfeld;Salim Roukos

  • Towards History-based Grammars: Using Richer Models for Probabilistic Parsing

    Ezra Black;Fred Jelinek;John Lafrerty;David M. Magerman

  • IBM's Statistical Question Answering system: TREC-10

    Abraham Ittycheriah;Martin Franz;Salim Roukos

  • Language Model Based Arabic Word Segmentation

    Young-Suk Lee;Kishore Papineni;Salim Roukos;Ossama Emam

Frequent Co-Authors

Frederick Jelinek
Frederick Jelinek Johns Hopkins University
Robert Leroy Mercer
Robert Leroy Mercer Renaissance Technologies
Vittorio Castelli
Vittorio Castelli IBM (United States)
John Lafferty
John Lafferty Yale University
Mo Yu
Mo Yu IBM (United States)
Alexander G. Gray
Alexander G. Gray Georgia Institute of Technology
Dimitri Kanevsky
Dimitri Kanevsky Google (United States)
Michael Picheny
Michael Picheny IBM (United States)
Chalapathy Neti
Chalapathy Neti IBM (United States)
Bhuvana Ramabhadran
Bhuvana Ramabhadran Google (United States)

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