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Entity recognition in the biomedical domain using a hybrid approach

Overview of attention for article published in Journal of Biomedical Semantics, November 2017
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Mentioned by

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2 tweeters

Citations

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27 Dimensions

Readers on

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53 Mendeley
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Title
Entity recognition in the biomedical domain using a hybrid approach
Published in
Journal of Biomedical Semantics, November 2017
DOI 10.1186/s13326-017-0157-6
Pubmed ID
Authors

Marco Basaldella, Lenz Furrer, Carlo Tasso, Fabio Rinaldi

Abstract

This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. These results are to our knowledge the best reported so far in this particular task.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 53 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Student > Master 10 19%
Researcher 9 17%
Other 3 6%
Student > Bachelor 3 6%
Other 4 8%
Unknown 11 21%
Readers by discipline Count As %
Computer Science 21 40%
Medicine and Dentistry 4 8%
Engineering 3 6%
Agricultural and Biological Sciences 3 6%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 6 11%
Unknown 14 26%

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 15 November 2017.
All research outputs
#7,589,282
of 12,145,106 outputs
Outputs from Journal of Biomedical Semantics
#195
of 312 outputs
Outputs of similar age
#158,868
of 281,964 outputs
Outputs of similar age from Journal of Biomedical Semantics
#6
of 13 outputs
Altmetric has tracked 12,145,106 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 312 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 20th percentile – i.e., 20% 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 281,964 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.