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Computer Science

D-Index
58
Citations
14621
World Ranking
3605
National Ranking
1730

Overview

Jacob Eisenstein is a researcher affiliated with Google in the United States, specializing in the field of Computer Science with a focus on Artificial Intelligence. Their scholarly contributions span multiple subfields including Computer Vision and Pattern Recognition, Sociology and Political Science, General Social Sciences, and Statistical and Nonlinear Physics.

The main topics covered in their work include:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Explainable Artificial Intelligence (XAI)
  • Multimodal Machine Learning Applications
  • Machine Learning and Algorithms
  • Computational and Text Analysis Methods
  • Complex Network Analysis Techniques

Jacob Eisenstein has been published extensively, with a significant number of papers appearing in venues such as:

  • arXiv (Cornell University)
  • Transactions of the Association for Computational Linguistics
  • Proceedings of the International AAAI Conference on Web and Social Media
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Journal of Cultural Analytics

Some of their recent papers include:

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning, 2020, arXiv (Cornell University)
  • Sparse, Dense, and Attentional Representations for Text Retrieval, 2021, Transactions of the Association for Computational Linguistics
  • Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond, 2022, Transactions of the Association for Computational Linguistics
  • Sparse, Dense, and Attentional Representations for Text Retrieval, 2020, arXiv (Cornell University)
  • Revisiting the Primacy of English in Zero-shot Cross-lingual Transfer, 2021, arXiv (Cornell University)

Throughout their research career, Jacob Eisenstein has collaborated frequently with a group of co-authors. Those with the most joint publications include:

  • Jonathan Berant
  • Victor Veitch
  • Chirag Nagpal
  • Kristina Toutanova
  • Diyi Yang

The focus areas of Eisenstein's work reflect a broad interdisciplinary approach, incorporating elements of artificial intelligence techniques and the social sciences. This multidisciplinary perspective is evident in their publication record and chosen research topics.

Best Publications

  • Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments

    Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das

  • A Latent Variable Model for Geographic Lexical Variation

    Jacob Eisenstein;Brendan O'Connor;Noah A. Smith;Eric P. Xing

  • Gender identity and lexical variation in social media

    David Bamman;Jacob Eisenstein;Tyler Schnoebelen

  • Explainable Prediction of Medical Codes from Clinical Text

    James Mullenbach;Sarah Wiegreffe;Jon Duke;Jimeng Sun

  • Underspecification Presents Challenges for Credibility in Modern Machine Learning

    Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam

  • You Can't Stay Here: The Efficacy of Reddit's 2015 Ban Examined Through Hate Speech

    Eshwar Chandrasekharan;Umashanthi Pavalanathan;Anirudh Srinivasan;Adam Glynn

  • What to do about bad language on the internet

    Jacob Eisenstein

  • Sparse Additive Generative Models of Text

    Jacob Eisenstein;Amr Ahmed;Eric P. Xing

  • Applying model-based techniques to the development of UIs for mobile computers

    Jacob Eisenstein;Jean Vanderdonckt;Angel Puerta

  • Towards a general computational framework for model-based interface development systems

    Angel R. Puerta;Jacob Eisenstein

  • The Internet's Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales

    Eshwar Chandrasekharan;Mattia Samory;Shagun Jhaver;Hunter Charvat

  • Bayesian Unsupervised Topic Segmentation

    Jacob Eisenstein;Regina Barzilay

  • Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

    Unknown

  • Representation Learning for Text-level Discourse Parsing

    Yangfeng Ji;Jacob Eisenstein

  • Diffusion of lexical change in social media.

    Jacob Eisenstein;Brendan O'Connor;Noah A. Smith;Eric P. Xing

  • Sparse, Dense, and Attentional Representations for Text Retrieval

    Yi Luan;Jacob Eisenstein;Kristina Toutanova;Michael Collins

  • Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics

    Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das

  • XIML: a common representation for interaction data

    Angel Puerta;Jacob Eisenstein

  • Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling

    Xiaochuang Han;Jacob Eisenstein

  • Better Document-level Sentiment Analysis from RST Discourse Parsing

    Parminder Bhatia;Yangfeng Ji;Jacob Eisenstein

  • Discriminative Improvements to Distributional Sentence Similarity

    Yangfeng Ji;Jacob Eisenstein

Frequent Co-Authors

Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Noah A. Smith
Noah A. Smith University of Washington
Maria Liakata
Maria Liakata Queen Mary University of London
Diyi Yang
Diyi Yang Stanford University
Jimeng Sun
Jimeng Sun University of Illinois at Urbana-Champaign
Sharon Goldwater
Sharon Goldwater University of Edinburgh
Cyrus Shahabi
Cyrus Shahabi University of Southern California
Munmun De Choudhury
Munmun De Choudhury Georgia Institute of Technology

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