Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis
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.
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.
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.
Keywords: Text mining; Event extraction; Network construction; Large-scale data; Distributed vector representations of words
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. 16(Suppl 16):S3.
VIB / UGent
Bioinformatics & Evolutionary Genomics
+32 (0) 9 33 13807 (phone)
+32 (0) 9 33 13809 (fax)