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Daniel S. Weld

Allen Institute of Artificial Intelligence

55 papers hereh-index 9447.4k citations357 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author26
  • last author28

Across the 55 of 55 papers where every author was matched, so the position is known.

fields
  • cs.CL21
  • cs.HC14
  • cs.AI11
  • cs.IR5
  • cs.DL4
affiliations
  • Allen Institute of Artificial Intelligence
  • University of Washington
Homepage
same name
  • Daniel S. Weld — 17 papers, h 8
  • Daniel S. Weld — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
19952024
most citedCORD-19: The COVID-19 Open Research Dataset

584 citations · 1.3k across the 49 of their papers we have counts for

collaborators
Showing 2022 · cs.CLShow all

4 papers · 2 filters

cs.CL2022

ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts

Sonia K. Murthy, Kyle Lo, Daniel King +7

Systems that can automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite backgro…

cs.CL2022

Few-shot Mining of Naturally Occurring Inputs and Outputs

Mandar Joshi, Terra Blevins, Mike Lewis +2

Creating labeled natural language training data is expensive and requires significant human effort. We mine input output examples from large corpora using a supervised mining funct…

cs.CL2022★ 4 cited

A Computational Inflection for Scientific Discovery

Tom Hope, Doug Downey, Oren Etzioni +2

We stand at the foot of a significant inflection in the trajectory of scientific discovery. As society continues on its fast-paced digital transformation, so does humankind's colle…

cs.CL2022★ 7 cited

Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search

Daniel King, Zejiang Shen, Nishant Subramani +3

Abstractive summarization systems today produce fluent and relevant output, but often "hallucinate" statements not supported by the source text. We analyze the connection between h…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.