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Towards One-Shot Learning for Text Classification using Inductive Logic Programming

Overview of attention for article published in Electronic Proceedings in Theoretical Computer Science, September 2023
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Article details
Title
Towards One-Shot Learning for Text Classification using Inductive Logic Programming
Published in
Electronic Proceedings in Theoretical Computer Science, September 2023
DOI 10.4204/eptcs.385.9
Authors

Ghazal Afroozi Milani, Daniel Cyrus, Alireza Tamaddoni-Nezhad

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 31 August 2023.
All research outputs
#21,914,735
of 24,451,065 outputs
Outputs from Electronic Proceedings in Theoretical Computer Science
#1,265
of 1,934 outputs
Outputs of similar age
#147,852
of 180,466 outputs
Outputs of similar age from Electronic Proceedings in Theoretical Computer Science
#15
of 46 outputs
Altmetric has tracked 24,451,065 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,934 research outputs from this source. They receive a mean Attention Score of 1.4. This one is in the 1st percentile – i.e., 1% 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 180,466 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.