Large-scale directional relationship extraction and resolution

Background: Relationships between entities such as genes, chemicals, metabolites, phenotypes and diseases in MEDLINE are often directional. That is, one may affect the other in a positive or negative manner. Detection of causality and direction is key in piecing pathways together and in examining possible implications of experimental results. Because of the size and growth of biomedical literature, it is increasingly important to be able to automate this process as much as possible. Results: Here we present a method of relation extraction using dependency graph parsing with SVM classification. We tested the SVM classifier first on gold standard corpora from GENIA and find it achieved 82% precision and 94.8% recall (F-measure of 87.9) on these standardized test sets. applied the entire system to all available MEDLINE abstracts for two target interactions with known effects. We find that while some directional relations are extracted with low ambiguity, others are apparently contradicto...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Cory B. Giles, Jonathan D. Wren
• Keywords: BMCBI 2008, Directional Relation, Gold Standard Corpora, Relation Extraction