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Attention Score in Context
| Title |
A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature
|
|---|---|
| Published in |
Journal of Cheminformatics, January 2015
|
| DOI | 10.1186/1758-2946-7-s1-s8 |
| Pubmed ID | |
| Authors |
Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, Hua Xu |
| Abstract |
Chemical compounds and drugs (together called chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of chemical entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of chemical entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
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 % |
|---|---|---|
| Germany | 2 | 3% |
| Unknown | 65 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 24% |
| Researcher | 11 | 16% |
| Student > Master | 9 | 13% |
| Student > Bachelor | 7 | 10% |
| Student > Doctoral Student | 6 | 9% |
| Other | 11 | 16% |
| Unknown | 7 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 34 | 51% |
| Medicine and Dentistry | 5 | 7% |
| Agricultural and Biological Sciences | 3 | 4% |
| Engineering | 3 | 4% |
| Linguistics | 2 | 3% |
| Other | 12 | 18% |
| Unknown | 8 | 12% |
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 27 March 2015.
All research outputs
#18,403,994
of 22,796,179 outputs
Outputs from Journal of Cheminformatics
#797
of 831 outputs
Outputs of similar age
#256,358
of 352,472 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 18 outputs
Altmetric has tracked 22,796,179 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 831 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. 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 352,472 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 18 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.