Extracting Protein-Protein Interaction from Biomedical Text Using Additional Shallow Parsing Information

This paper explores protein-protein interaction extraction from biomedical literature using support vector machines (SVM). Besides common lexical features, various overlap features and base phrase chunking information are used to improve the performance. Evaluation on the AIMed corpus shows that our feature-based method achieves very encouraging performances of 68.6 and 51.0 in F-measure with 10-fold pair-wise cross-validation and 10-fold document-wise cross-validation respectively, which are comparable with other state-of-the-art feature-based methods.