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Attention Score in Context
| Title |
Evaluating gold standard corpora against gene/protein tagging solutions and lexical resources
|
|---|---|
| Published in |
Journal of Biomedical Semantics, October 2013
|
| DOI | 10.1186/2041-1480-4-28 |
| Pubmed ID | |
| Authors |
Dietrich Rebholz-Schuhmann, Senay Kafkas, Jee-Hyub Kim, Chen Li, Antonio Jimeno Yepes, Robert Hoehndorf, Rolf Backofen, Ian Lewin |
| Abstract |
The identification of protein and gene names (PGNs) from the scientific literature requires semantic resources: Terminological and lexical resources deliver the term candidates into PGN tagging solutions and the gold standard corpora (GSC) train them to identify term parameters and contextual features. Ideally all three resources, i.e. corpora, lexica and taggers, cover the same domain knowledge, and thus support identification of the same types of PGNs and cover all of them. Unfortunately, none of the three serves as a predominant standard and for this reason it is worth exploring, how these three resources comply with each other. We systematically compare different PGN taggers against publicly available corpora and analyze the impact of the included lexical resource in their performance. In particular, we determine the performance gains through false positive filtering, which contributes to the disambiguation of identified PGNs. |
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 % |
|---|---|---|
| Spain | 1 | 50% |
| Switzerland | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 4% |
| Croatia | 1 | 4% |
| Netherlands | 1 | 4% |
| Unknown | 24 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 26% |
| Student > Bachelor | 4 | 15% |
| Other | 4 | 15% |
| Student > Ph. D. Student | 3 | 11% |
| Student > Master | 3 | 11% |
| Other | 3 | 11% |
| Unknown | 3 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 33% |
| Agricultural and Biological Sciences | 6 | 22% |
| Engineering | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Psychology | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 4 | 15% |
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 October 2022.
All research outputs
#17,285,036
of 25,373,627 outputs
Outputs from Journal of Biomedical Semantics
#240
of 368 outputs
Outputs of similar age
#140,296
of 223,709 outputs
Outputs of similar age from Journal of Biomedical Semantics
#11
of 16 outputs
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So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.