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X Demographics
Mendeley readers
Attention Score in Context
| 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
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
#13,671,297
of 22,681,577 outputs
Outputs from BMC Bioinformatics
#4,435
of 7,250 outputs
Outputs of similar age
#134,461
of 160,893 outputs
Outputs of similar age from BMC Bioinformatics
#43
of 52 outputs
Altmetric has tracked 22,681,577 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,250 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% 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 160,893 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.