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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Detecting modification of biomedical events using a deep parsing approach
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, April 2012
|
| DOI | 10.1186/1472-6947-12-s1-s4 |
| Pubmed ID | |
| Authors |
Andrew MacKinlay, David Martinez, Timothy Baldwin |
| Abstract |
This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| France | 1 | 6% |
| Unknown | 14 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 25% |
| Student > Bachelor | 3 | 19% |
| Student > Master | 2 | 13% |
| Lecturer | 2 | 13% |
| Other | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 2 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 19% |
| Medicine and Dentistry | 2 | 13% |
| Chemistry | 2 | 13% |
| Linguistics | 2 | 13% |
| Philosophy | 1 | 6% |
| Other | 2 | 13% |
| Unknown | 4 | 25% |
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 30 May 2012.
All research outputs
#18,306,425
of 22,665,794 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,561
of 1,978 outputs
Outputs of similar age
#125,550
of 162,571 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#34
of 40 outputs
Altmetric has tracked 22,665,794 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 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 162,571 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 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.