| Title |
Biomedical named entity extraction: some issues of corpus compatibilities
|
|---|---|
| Published in |
SpringerPlus, November 2013
|
| DOI | 10.1186/2193-1801-2-601 |
| Pubmed ID | |
| Authors |
Asif Ekbal, Sriparna Saha, Utpal Kumar Sikdar |
| Abstract |
Named Entity (NE) extraction is one of the most fundamental and important tasks in biomedical information extraction. It involves identification of certain entities from text and their classification into some predefined categories. In the biomedical community, there is yet no general consensus regarding named entity (NE) annotation; thus, it is very difficult to compare the existing systems due to corpus incompatibilities. Due to this problem we can not also exploit the advantages of using different corpora together. In our present work we address the issues of corpus compatibilities, and use a single objective optimization (SOO) based classifier ensemble technique that uses the search capability of genetic algorithm (GA) for NE extraction in biomedicine. We hypothesize that the reliability of predictions of each classifier differs among the various output classes. We use Conditional Random Field (CRF) and Support Vector Machine (SVM) frameworks to build a number of models depending upon the various representations of the set of features and/or feature templates. It is to be noted that we tried to extract the features without using any deep domain knowledge and/or resources. |
Mendeley readers
The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 5% |
| Unknown | 19 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 25% |
| Student > Master | 3 | 15% |
| Lecturer | 2 | 10% |
| Student > Bachelor | 2 | 10% |
| Student > Postgraduate | 2 | 10% |
| Other | 5 | 25% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 40% |
| Social Sciences | 3 | 15% |
| Medicine and Dentistry | 2 | 10% |
| Business, Management and Accounting | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Other | 3 | 15% |
| Unknown | 2 | 10% |
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 12 November 2013.
All research outputs
#15,284,663
of 22,729,647 outputs
Outputs from SpringerPlus
#932
of 1,853 outputs
Outputs of similar age
#130,412
of 212,425 outputs
Outputs of similar age from SpringerPlus
#54
of 101 outputs
Altmetric has tracked 22,729,647 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,853 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 35th percentile – i.e., 35% 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 212,425 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.