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Adaptable, high recall, event extraction system with minimal configuration

Overview of attention for article published in BMC Bioinformatics, June 2015
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Title
Adaptable, high recall, event extraction system with minimal configuration
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/1471-2105-16-s10-s7
Pubmed ID
Authors

Makoto Miwa, Sophia Ananiadou

Abstract

Biomedical event extraction has been a major focus of biomedical natural language processing (BioNLP) research since the first BioNLP shared task was held in 2009. Accordingly, a large number of event extraction systems have been developed. Most such systems, however, have been developed for specific tasks and/or incorporated task specific settings, making their application to new corpora and tasks problematic without modification of the systems themselves. There is thus a need for event extraction systems that can achieve high levels of accuracy when applied to corpora in new domains, without the need for exhaustive tuning or modification, whilst retaining competitive levels of performance. We have enhanced our state-of-the-art event extraction system, EventMine, to alleviate the need for task-specific tuning. Task-specific details are specified in a configuration file, while extensive task-specific parameter tuning is avoided through the integration of a weighting method, a covariate shift method, and their combination. The task-specific configuration and weighting method have been employed within the context of two different sub-tasks of BioNLP shared task 2013, i.e. Cancer Genetics (CG) and Pathway Curation (PC), removing the need to modify the system specifically for each task. With minimal task specific configuration and tuning, EventMine achieved the 1st place in the PC task, and 2nd in the CG, achieving the highest recall for both tasks. The system has been further enhanced following the shared task by incorporating the covariate shift method and entity generalisations based on the task definitions, leading to further performance improvements. We have shown that it is possible to apply a state-of-the-art event extraction system to new tasks with high levels of performance, without having to modify the system internally. Both covariate shift and weighting methods are useful in facilitating the production of high recall systems. These methods and their combination can adapt a model to the target data with no deep tuning and little manual configuration.

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 4%
Belarus 1 4%
Unknown 24 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 23%
Student > Master 5 19%
Student > Bachelor 4 15%
Researcher 4 15%
Professor > Associate Professor 3 12%
Other 3 12%
Unknown 1 4%
Readers by discipline Count As %
Computer Science 12 46%
Medicine and Dentistry 4 15%
Nursing and Health Professions 2 8%
Agricultural and Biological Sciences 1 4%
Business, Management and Accounting 1 4%
Other 2 8%
Unknown 4 15%

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 29 July 2015.
All research outputs
#17,272,796
of 19,510,965 outputs
Outputs from BMC Bioinformatics
#6,202
of 6,616 outputs
Outputs of similar age
#202,253
of 245,097 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
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