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Benchmarking natural-language parsers for biological applications using dependency graphs

Overview of attention for article published in BMC Bioinformatics, January 2007
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

twitter
2 tweeters
linkedin
1 LinkedIn user

Citations

dimensions_citation
50 Dimensions

Readers on

mendeley
67 Mendeley
citeulike
11 CiteULike
connotea
3 Connotea
Title
Benchmarking natural-language parsers for biological applications using dependency graphs
Published in
BMC Bioinformatics, January 2007
DOI 10.1186/1471-2105-8-24
Pubmed ID
Authors

Andrew B Clegg, Adrian J Shepherd

Abstract

Interest is growing in the application of syntactic parsers to natural language processing problems in biology, but assessing their performance is difficult because differences in linguistic convention can falsely appear to be errors. We present a method for evaluating their accuracy using an intermediate representation based on dependency graphs, in which the semantic relationships important in most information extraction tasks are closer to the surface. We also demonstrate how this method can be easily tailored to various application-driven criteria.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
Germany 2 3%
Spain 2 3%
Brazil 2 3%
France 1 1%
New Caledonia 1 1%
Sweden 1 1%
Unknown 54 81%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 22%
Student > Ph. D. Student 12 18%
Student > Master 11 16%
Professor 7 10%
Professor > Associate Professor 6 9%
Other 15 22%
Unknown 1 1%
Readers by discipline Count As %
Computer Science 31 46%
Agricultural and Biological Sciences 14 21%
Linguistics 7 10%
Medicine and Dentistry 4 6%
Neuroscience 2 3%
Other 3 4%
Unknown 6 9%

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 13 February 2014.
All research outputs
#6,869,773
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#2,613
of 4,576 outputs
Outputs of similar age
#60,426
of 129,323 outputs
Outputs of similar age from BMC Bioinformatics
#25
of 59 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 42nd percentile – i.e., 42% 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 129,323 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 52% of its contemporaries.
We're also able to compare this research output to 59 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 55% of its contemporaries.