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Negated bio-events: analysis and identification

Overview of attention for article published in BMC Bioinformatics, January 2013
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (71st percentile)

Mentioned by

twitter
8 tweeters

Citations

dimensions_citation
34 Dimensions

Readers on

mendeley
48 Mendeley
citeulike
5 CiteULike
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Title
Negated bio-events: analysis and identification
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-14
Pubmed ID
Authors

Raheel Nawaz, Paul Thompson, Sophia Ananiadou

Abstract

Negation occurs frequently in scientific literature, especially in biomedical literature. It has previously been reported that around 13% of sentences found in biomedical research articles contain negation. Historically, the main motivation for identifying negated events has been to ensure their exclusion from lists of extracted interactions. However, recently, there has been a growing interest in negative results, which has resulted in negation detection being identified as a key challenge in biomedical relation extraction. In this article, we focus on the problem of identifying negated bio-events, given gold standard event annotations.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 4%
United Kingdom 2 4%
Brazil 1 2%
Australia 1 2%
France 1 2%
Netherlands 1 2%
United States 1 2%
Unknown 39 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 31%
Student > Ph. D. Student 10 21%
Student > Master 5 10%
Student > Doctoral Student 4 8%
Professor 3 6%
Other 9 19%
Unknown 2 4%
Readers by discipline Count As %
Computer Science 29 60%
Agricultural and Biological Sciences 5 10%
Biochemistry, Genetics and Molecular Biology 4 8%
Social Sciences 3 6%
Medicine and Dentistry 2 4%
Other 2 4%
Unknown 3 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 17 January 2013.
All research outputs
#3,360,019
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#1,390
of 5,420 outputs
Outputs of similar age
#50,121
of 246,283 outputs
Outputs of similar age from BMC Bioinformatics
#55
of 193 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 74% of its peers.
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 246,283 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 193 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.