World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
12461
World Ranking
9511
National Ranking
4028

Overview

Daniel Gildea is a researcher affiliated with the University of Rochester in the United States, with a broad focus encompassing computer science and medicine. Their work spans multiple subfields, including artificial intelligence, computer vision and pattern recognition, pulmonary and respiratory medicine, molecular biology, and surgery.

Their research covers a range of topics within these fields. Key areas include natural language processing techniques, topic modeling, multimodal machine learning applications, algorithms and data compression, advanced graph neural networks, speech and dialogue systems, and neonatal respiratory health research.

Gildea has authored several papers published in various academic venues. Some recent works are:

  • Evidence Integration for Multi-Hop Reading Comprehension With Graph Neural Networks, 2020, IEEE Transactions on Knowledge and Data Engineering
  • Hierarchical Context Tagging for Utterance Rewriting, 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • Derivation of a Natural Language Processing Algorithm to Identify Febrile Infants, 2022, Journal of Hospital Medicine
  • Unsupervised Bilingual Lexicon Induction Across Writing Systems, 2020, arXiv (Cornell University)
  • AWLCO: All-Window Length Co-Occurrence, 2021, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Frequent co-authors collaborating with Daniel Gildea include Lisa Jin, Linfeng Song, Joshua Sobel, Noah Bertram, and Chen Ding.

The main publication venues where Gildea's work has appeared are:

  • arXiv (Cornell University)
  • IEEE Transactions on Knowledge and Data Engineering
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Hospital Medicine
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Best Publications

  • The Proposition Bank: An Annotated Corpus of Semantic Roles

    Martha Palmer;Daniel Gildea;Paul Kingsbury

  • Automatic labeling of semantic roles

    Daniel Gildea;Daniel Jurafsky

  • Effects of disfluencies, predictability, and utterance position on word form variation in English conversation

    Alan Bell;Daniel Jurafsky;Eric Fosler-Lussier;Cynthia Girand

  • Corpus Variation and Parser Performance

    Daniel Gildea

  • A Smorgasbord of Features for Statistical Machine Translation

    Franz Josef Och;Daniel Gildea;Sanjeev Khudanpur;Anoop Sarkar

  • Topic-based language models using EM.

    Daniel Gildea;Thomas Hofmann

  • The Necessity of Parsing for Predicate Argument Recognition

    Daniel Gildea;Martha Palmer

  • Loosely Tree-Based Alignment for Machine Translation

    Daniel Gildea

  • A Graph-to-Sequence Model for AMR-to-Text Generation

    Linfeng Song;Yue Zhang;Zhiguo Wang;Daniel Gildea

  • Syntactic Features for Evaluation of Machine Translation

    Ding Liu;Daniel Gildea

  • Do Grammars Minimize Dependency Length

    Daniel Gildea;David Temperley

  • N-ary Relation Extraction using Graph-State LSTM

    Linfeng Song;Yue Zhang;Zhiguo Wang;Daniel Gildea

  • Synchronous Binarization for Machine Translation

    Hao Zhang;Liang Huang;Daniel Gildea;Kevin Knight

  • Leveraging Context Information for Natural Question Generation

    Linfeng Song;Zhiguo Wang;Wael Hamza;Yue Zhang

  • Semantic Neural Machine Translation using AMR

    Linfeng Song;Daniel Gildea;Yue Zhang;Zhiguo Wang

  • Automated Analysis and Prediction of Job Interview Performance

    Iftekhar Naim;Md. Iftekhar Tanveer;Daniel Gildea;Mohammed Ehsan Hoque

  • Identifying semantic roles using Combinatory Categorial Grammar

    Daniel Gildea;Julia Hockenmaier

  • Automated prediction and analysis of job interview performance: The role of what you say and how you say it

    Iftekhar Naim;M. Iftekhar Tanveer;Daniel Gildea;Mohammed Ehsan Hoque

  • Semantic Role Features for Machine Translation

    Ding Liu;Daniel Gildea

  • Learning bias and phonological-rule induction

    Daniel Gildea;Daniel Jurafsky

  • Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks.

    Linfeng Song;Zhiguo Wang;Mo Yu;Yue Zhang

Frequent Co-Authors

Yue Zhang
Yue Zhang Westlake University
Dan Jurafsky
Dan Jurafsky Stanford University
James F. Allen
James F. Allen University of Rochester
Liang Huang
Liang Huang Oregon State University
Mo Yu
Mo Yu IBM (United States)
Henry Kautz
Henry Kautz University of Virginia
Jiebo Luo
Jiebo Luo University of Rochester
Jinsong Su
Jinsong Su Xiamen University
Kevin Knight
Kevin Knight University of Southern California
Martha Palmer
Martha Palmer University of Colorado Boulder

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