A set of domain rules and a deep network for protein coreference resolution

Database : the Journal of Biological Databases and Curation
Chen LiXiangrong Zhang

Abstract

Current research of bio-text mining mainly focuses on event extractions. Biological networks present much richer and meaningful information to biologists than events. Bio-entity coreference resolution (CR) is a very important method to complete a bio-event's attributes and interconnect events into bio-networks. Though general CR methods have been studies for a long time, they could not produce a practically useful result when applied to a special domain. Therefore, bio-entity CR needs attention to better assist biological network extraction. In this article, we present two methods for bio-entity CR. The first is a rule-based method, which creates a set of syntactic rules or semantic constraints for CR. It obtains a state-of-the-art performance (an F1-score of 62.0%) on the community supported dataset. We also present a machine learning-based method, which takes use of a recurrent neural network model, a long-short term memory network. It automatically learns global discriminative representations of all kinds of coreferences without hand-crafted features. The model outperforms the previously best machine leaning-based method.

References

Dec 13, 2002·Health Policy and Planning·Ramon A CastanoLuis G Morales
Dec 15, 2011·Journal of Biomedical Semantics·Katsumasa YoshikawaYuji Matsumoto
Nov 20, 2012·BMC Bioinformatics·Ngan NguyenJunichi Tsujii
Feb 26, 2013·Briefings in Bioinformatics·Chen LiDietrich Rebholz-Schuhmann
Mar 2, 2016·Journal of Biomedical Informatics·Miji ChoiKarin Verspoor

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