Semantic role labeling as sequential tagging

In this paper we present a semantic role labeling system submitted to the CoNLL-2005 shared task. The system makes use of partial and full syntactic information and converts the task into a sequential BIO-tagging. As a result, the labeling architecture is very simple. Building on a state-of-the-art set of features, a binary classifier for each label is trained using AdaBoost with fixed depth decision trees. The final system, which combines the outputs of two base systems performed F1=76.59 on the official test set. Additionally, we provide results comparing the system when using partial vs. full parsing input information.