Semantic Role Labeling Based on Dependency Tree with Multi-features

In this paper, a dependency tree-based semantic role labeling (SRL) system is proposed. Firstly, this paper introduces current SRL research situation, analyses syntactic tree-based SRL and dependency tree-based SRL comparatively. System accomplishes predicate identification, and automatically creates dependency relation using a dependency parser. Then, system cuts off the nodes which are not related with the predicate using effective pruning algorithm, and proposes additional features based on Hacioglupsilas baseline features. Finally, the features are input to maximum entropy classifier to determine the corresponding semantic role label. System achieves the F1-measure of 81.95 on the WSJ corpus of the CoNLLpsila2008 SRL shared task.