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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Modeling and mining term association for improving biomedical information retrieval performance
|
|---|---|
| Published in |
BMC Bioinformatics, June 2012
|
| DOI | 10.1186/1471-2105-13-s9-s2 |
| Pubmed ID | |
| Authors |
Qinmin Hu, Jimmy Xiangji Huang, Xiaohua Hu |
| Abstract |
The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. |
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 % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 9% |
| Unknown | 10 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 1 | 9% |
| Lecturer | 1 | 9% |
| Student > Doctoral Student | 1 | 9% |
| Student > Bachelor | 1 | 9% |
| Professor | 1 | 9% |
| Other | 4 | 36% |
| Unknown | 2 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 5 | 45% |
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Arts and Humanities | 1 | 9% |
| Business, Management and Accounting | 1 | 9% |
| Engineering | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 2 | 18% |
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 01 May 2013.
All research outputs
#18,337,420
of 22,708,120 outputs
Outputs from BMC Bioinformatics
#6,291
of 7,256 outputs
Outputs of similar age
#129,103
of 167,369 outputs
Outputs of similar age from BMC Bioinformatics
#85
of 106 outputs
Altmetric has tracked 22,708,120 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 7,256 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 5th percentile – i.e., 5% 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 167,369 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 106 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.