A general instance representation architecture for protein-protein interaction extraction

Previous researches have shown that supervised Protein-Protein Interaction Extraction (PPIE) can get high accuracies with elaborately selected features and kernels. However, most features and kernels rest upon domain knowledge and natural language analysis, which makes the supervised model expensive, heavy and brittle. Moreover, the one-hot encoding, a commonly used representation technique, fails to capture the semantic similarity between words. To reduce the manual labor and overcome the shortage of one-hot encoding, we put forward a general instance representation architecture for PPIE, which integrates word representation and vector composition. Our method obtains F-scores of 69.4%, 78.8%, 76.0%, 74.0% and 81.1% on AIMed, BioInfer, HPRD50, IEPA and LLL respectively.