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A resampling-based method to evaluate NLI models

Overview of attention for article published in Natural Language Engineering, June 2023
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
3 X users

Readers on

mendeley
4 Mendeley
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Title
A resampling-based method to evaluate NLI models
Published in
Natural Language Engineering, June 2023
DOI 10.1017/s1351324923000268
Authors

Felipe de Souza Salvatore, Marcelo Finger, Roberto Hirata, Alexandre G. Patriota

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 1 25%
Unknown 3 75%
Readers by discipline Count As %
Unknown 4 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 June 2023.
All research outputs
#15,692,440
of 25,639,676 outputs
Outputs from Natural Language Engineering
#154
of 219 outputs
Outputs of similar age
#180,153
of 386,562 outputs
Outputs of similar age from Natural Language Engineering
#3
of 6 outputs
Altmetric has tracked 25,639,676 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 219 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 386,562 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.