Application of the EVEX resource to event extraction and network construction : shared task entry and result analysis

Show simple item record Hakala, Kai Van Landeghem, Sofie Salakoski, Tapio Van de Peer, Yves Ginter, Filip 2016-02-16T07:22:09Z 2016-02-16T07:22:09Z 2015-10-30
dc.description.abstract BACKGROUND : 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 this paper, we describe our participation in the latest BioNLP Shared Task using the large-scale text mining resource EVEX. We participated in the Genia Event Extraction (GE) and Gene Regulation Network (GRN) tasks with two separate systems. In the GE task, we implemented a re-ranking approach to improve the precision of an existing event extraction system, incorporating features from the EVEX resource. In the GRN task, our system relied solely on the EVEX resource and utilized a rule-based conversion algorithm between the EVEX and GRN formats. RESULTS : 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. In the GRN task, we ranked fifth in the official results with a strict/relaxed SER score of 0.92/0.81 respectively. To try and improve upon these results, we have implemented a novel machine learning based conversion system and benchmarked its performance against the original rule-based system. CONCLUSIONS : 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. In the GE task we demonstrate that both the re-ranking approach and the word vectors can provide slight performance improvement. A manual evaluation of the re-ranking results pinpoints some of the challenges faced in applying large-scale text mining knowledge to event extraction. en_ZA
dc.description.librarian am2015 en_ZA
dc.description.sponsorship Computational resources were provided by CSC IT Center for Science Ltd., Espoo, Finland. The work of KH and FG was supported by the Academy of Finland, and of SVL by the Research Foundation Flanders (FWO). YVdP and SVL acknowledge the support from Ghent University (Multidisciplinary Research Partnership Bioinformatics: from nucleotides to networks). en_ZA
dc.description.uri en_ZA
dc.identifier.citation Hakala, K, Van Landeghem, S, Salakoski, T, Van de Peer, Y & Ginter, F 2015, 'Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis', BMC Bioinformatics, vol. 16, suppl 16, S3, pp. 1-12. en_ZA
dc.identifier.issn 1471-2105
dc.identifier.other 10.1186/1471-2105-16-S16-S3
dc.language.iso en en_ZA
dc.publisher BioMed Central en_ZA
dc.rights © 2015 Hakala et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License. en_ZA
dc.subject Mining en_ZA
dc.subject Biomedical literature en_ZA
dc.subject EVEX resource en_ZA
dc.subject Genia Event Extraction (GE) en_ZA
dc.subject Gene Regulation Network (GRN) en_ZA
dc.title Application of the EVEX resource to event extraction and network construction : shared task entry and result analysis en_ZA
dc.type Article en_ZA

Files in this item

This item appears in the following Collection(s)

Show simple item record