↓ Skip to main content

Wide-coverage relation extraction from MEDLINE using deep syntax

Overview of attention for article published in BMC Bioinformatics, April 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)

Mentioned by

9 tweeters
1 Facebook page


14 Dimensions

Readers on

60 Mendeley
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Wide-coverage relation extraction from MEDLINE using deep syntax
Published in
BMC Bioinformatics, April 2015
DOI 10.1186/s12859-015-0538-8
Pubmed ID

Nhung TH Nguyen, Makoto Miwa, Yoshimasa Tsuruoka, Takashi Chikayama, Satoshi Tojo


Relation extraction is a fundamental technology in biomedical text mining. Most of the previous studies on relation extraction from biomedical literature have focused on specific or predefined types of relations, which inherently limits the types of the extracted relations. With the aim of fully leveraging the knowledge described in the literature, we address much broader types of semantic relations using a single extraction framework. Our system, which we name PASMED, extracts diverse types of binary relations from biomedical literature using deep syntactic patterns. Our experimental results demonstrate that it achieves a level of recall considerably higher than the state of the art, while maintaining reasonable precision. We have then applied PASMED to the whole MEDLINE corpus and extracted more than 137 million semantic relations. The extracted relations provide a quantitative understanding of what kinds of semantic relations are actually described in MEDLINE and can be ultimately extracted by (possibly type-specific) relation extraction systems. PASMED extracts a large number of relations that have previously been missed by existing text mining systems. The entire collection of the relations extracted from MEDLINE is publicly available in machine-readable form, so that it can serve as a potential knowledge base for high-level text-mining applications.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
Japan 1 2%
Belarus 1 2%
Unknown 56 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 30%
Student > Ph. D. Student 15 25%
Student > Master 6 10%
Student > Bachelor 5 8%
Professor > Associate Professor 4 7%
Other 11 18%
Unknown 1 2%
Readers by discipline Count As %
Computer Science 26 43%
Medicine and Dentistry 7 12%
Agricultural and Biological Sciences 7 12%
Biochemistry, Genetics and Molecular Biology 3 5%
Engineering 3 5%
Other 12 20%
Unknown 2 3%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 11 June 2015.
All research outputs
of 15,466,176 outputs
Outputs from BMC Bioinformatics
of 5,648 outputs
Outputs of similar age
of 227,252 outputs
Outputs of similar age from BMC Bioinformatics
of 1 outputs
Altmetric has tracked 15,466,176 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,648 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 77% of its peers.
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 227,252 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them