Extracting Gene Regulation Networks Using Linear-Chain Conditional Random Fields and Rules @ACL 2013, BioNLP Workshop

Saturday, 03 August 2013 23:01 Marinka
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Labels: Computer ScienceMathsBioinformaticsMachine LearningInformation Retrieval

This week Slavko Zitnik will present our paper (he is the first author) at ACLACL BioNLP Workshop on extending linear-chain conditional random fields (CRF) with skip-mentions to extract gene regulatory networks from biomedical literature and a sieve-based system architecture, which is the complete pipeline of data processing that includes data preparation, linear-chain CRF and rule based relation detection and data cleaning.

Published literature in molecular genetics may collectively provide much information on gene regulation networks. Dedicated computational approaches are required to sip through large volumes of text and infer gene interactions. We propose a novel sieve-based relation extraction system that uses linear-chain conditional random fields and rules. Also, we introduce a new skip-mention data representation to enable distant relation extraction using first-order models. To account for a variety of relation types, multiple models are inferred. The system was applied to the BioNLP 2013 Gene Regulation Network Shared Task. Our approach was ranked first of five, with a slot error rate of 0.73.

Presentation slides.

Last Updated on Sunday, 25 August 2013 21:40