Title |
Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis
|
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Published in |
BMC Bioinformatics, October 2015
|
DOI | 10.1186/1471-2105-16-s16-s3 |
Pubmed ID | |
Authors |
Kai Hakala, Sofie Van Landeghem, Tapio Salakoski, Yves Van de Peer, Filip Ginter |
Abstract |
Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems. In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge. For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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France | 1 | 5% |
Unknown | 21 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 4 | 18% |
Student > Bachelor | 3 | 14% |
Student > Ph. D. Student | 3 | 14% |
Student > Postgraduate | 3 | 14% |
Student > Master | 2 | 9% |
Other | 2 | 9% |
Unknown | 5 | 23% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 4 | 18% |
Computer Science | 4 | 18% |
Biochemistry, Genetics and Molecular Biology | 3 | 14% |
Decision Sciences | 2 | 9% |
Nursing and Health Professions | 1 | 5% |
Other | 3 | 14% |
Unknown | 5 | 23% |