Paper: Semantic Role Labeling As Sequential Tagging

ACL ID W05-0628
Title Semantic Role Labeling As Sequential Tagging
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2005

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 informa- tion and converts the task into a sequen- tial BIO-tagging. As a result, the label- ing architecture is very simple. Build- ing 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. Addi- tionally, we provide results comparing the system when using partial vs. full parsing input information. 1 Goals and System Architecture The goal of our work is twofold. On the one hand, we want to test whether it is possible to implement a competitive SRL system ...