Paper: Semantic Role Labeling System Using Maximum Entropy Classifier

ACL ID W05-0627
Title Semantic Role Labeling System Using Maximum Entropy Classifier
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005
Authors

A maximum entropy classi er is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classi er is trained to identify and classify the predi- cates’ semantic arguments together. Only the constituents with the largest probabil- ity among embedding ones are kept. Af- ter predicting all arguments which have matching constituents in full parsing trees, a simple rule-based post-processing is ap- plied to correct the arguments which have no matching constituents in these trees. Some useful features and their combina- tions are evaluated.