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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 2019 · cs.CLShow all

4 papers · 2 filters

cs.CL2019★ 5 cited

S2ORC: The Semantic Scholar Open Research Corpus

Kyle Lo, Lucy Lu Wang, Mark Neumann +2

We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines. The corpus consists of rich metadata, paper abstracts, resolved bib…

cs.CL2019★ 117 cited

Pretrained Language Models for Sequential Sentence Classification

Arman Cohan, Iz Beltagy, Daniel King +2

As a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding s…

cs.CL2019

BERT for Coreference Resolution: Baselines and Analysis

Mandar Joshi, Omer Levy, Daniel S. Weld +1

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicat…

cs.CL2019

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Mandar Joshi, Danqi Chen, Yinhan Liu +3

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather…

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