Paper: How Feasible and Robust is the Automatic Extraction of Gene Regulation Events? A Cross-Method Evaluation under Lab and Real-Life Conditions

ACL ID W09-1305
Title How Feasible and Robust is the Automatic Extraction of Gene Regulation Events? A Cross-Method Evaluation under Lab and Real-Life Conditions
Venue Workshop on Biomedical Natural Language Processing
Session
Year 2009
Authors

We explore a rule system and a machine learn- ing (ML) approach to automatically harvest information on gene regulation events (GREs) from biological documents in two different evaluation scenarios – one uses self-supplied corpora in a clean lab setting, while the other incorporates a standard reference database of curated GREs from REGULONDB, real-life data generated independently from our work. In the lab condition, we test how feasible the automatic extraction of GREs really is and achieve F-scores, under different, not di- rectly comparable test conditions though, for the rule and the ML systems which amount to 34% and 44%, respectively. In the REGU- LONDB condition, we investigate how robust both methodologies are by comparing them with this routinely used database. Here, the best F...