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A neural joint model for entity and relation extraction from biomedical text

Overview of attention for article published in BMC Bioinformatics, March 2017
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Title
A neural joint model for entity and relation extraction from biomedical text
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1609-9
Pubmed ID
Authors

Fei Li, Meishan Zhang, Guohong Fu, Donghong Ji

Abstract

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 <1%
Unknown 185 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 26%
Student > Master 27 15%
Researcher 25 13%
Student > Postgraduate 12 6%
Student > Bachelor 11 6%
Other 32 17%
Unknown 31 17%
Readers by discipline Count As %
Computer Science 112 60%
Agricultural and Biological Sciences 6 3%
Biochemistry, Genetics and Molecular Biology 5 3%
Mathematics 5 3%
Engineering 4 2%
Other 15 8%
Unknown 39 21%

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 03 April 2017.
All research outputs
#7,132,273
of 9,339,536 outputs
Outputs from BMC Bioinformatics
#3,284
of 3,948 outputs
Outputs of similar age
#188,448
of 261,110 outputs
Outputs of similar age from BMC Bioinformatics
#75
of 92 outputs
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