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David King

David King has 2 papers in our library, in 2016–2017, most of them on Universal Dependencies, Sentiment & emotion, Text generation; often with Michael White, Marie-Catherine de Marneffe, Micha Elsner.

2 papers in our library · 2016–2017

OpenAlex · ORCID · h-index 5 · 10 works · 76 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 29 citations in our library · 3 more in our library without a page yet · ⭐ Paper Star rank #4492

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

🎓The Ohio State UniversityColumbus · United States2015–2020 · 9 paperscurrent

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.

 Topics

 Universal Dependencies (1) Sentiment & emotion (1) Text generation (1)

 Works by year

2010s

20171 paper20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems (2017 · 34 citations)
    The authors, a team from The Ohio State University, took part as "breakers" in the "Build It Break It" sentiment analysis competition held at EMNLP in 2017.
  2. Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization (2016 · 1 citation)
    Grammar-based realisers work well when their input carries long-distance dependencies and coordination, but standard UD conversions from the Penn Treebank lose these, because the conversion ignores the traces.

Also in our library

3 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

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

 Cited by

Papers of our library cite their works 29 times (of the 140,884 papers whose bibliographies we read); 8 citing papers with a page here →.

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