Semantic role Lableing system using maximum entropy Classifier

Abstract

A maximum entropy classifier is used in our semantic role labeling system, which takes syntactic constituents as the labeling units. The maximum entropy classifier is trained to identify and classify the predicates' semantic arguments together. Only the constituents with the largest probability among embedding ones are kept. After predicting all arguments which have matching constituents in full parsing trees, a simple rule-based post-processing is applied to correct the arguments which have no matching constituents in these trees. Some useful features and their combinations are evaluated. © 2005 Association for Computational Linguistics.