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Jeffrey Ling

Jeffrey Ling has 2 papers in our library, in 2016–2019, most of them on Knowledge graphs & ontologies; often with Alexander M. Rush, Livio Baldini Soares, Nicholas FitzGerald.

2 papers in our library · 2016–2019

OpenAlex · h-index 9 · 11 works · 1,149 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 380 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #2559

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢GoogleMountain View · United States2019–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 workComputational Theory and Mathematics · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Knowledge graphs & ontologies · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Distributional semantics · 1 work

 Works by year

2010s

20191 paper20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Matching the Blanks: Distributional Similarity for Relation Learning (2019 · 999 citations)
    The paper addresses the task of extracting relations between entities in text. The authors aim to obtain general-purpose relation representations that do not depend on a fixed schema or a large knowledge graph. First, using BERT LARGE, they compare ways to mark the two target entities in the input and to obtain a…
  2. Image-to-Markup Generation with Coarse-to-Fine Attention (2016 · 116 citations)
    The paper treats recovering LaTeX source from an image of a typeset formula as a form of structured OCR. Unlike plain text recognition, the output need not follow the image left to right, because subscripts, fractions and nesting must be encoded. The authors build a public benchmark, IM2LATEX-100K, of formulas taken…

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Matching the Blanks: Distributional Similarity for Relation LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, et al.2019 · 999 citations
  2. Fusion of Detected Objects in Text for Visual Question Answering ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yetChris Alberti et al.2019 · 218 citations
  3. Image-to-Markup Generation with Coarse-to-Fine AttentionarXiv:1609.04938Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, et al.2016 · 116 citations
  4. Scene Transformer: A unified multi-task model for behavior prediction and planning ↗arXiv (Cornell University) · no summary yetJiquan Ngiam et al.2021 · 15 citations

 Co-authors here

5 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 380 times (of the 149,439 papers whose bibliographies we read); 42 citing papers with a page here →.

Updated

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