A Tree Kernel-Based Shallow Semantic Parser for Thematic Role Extraction

We present a simple, two-steps supervised strategy for the identification and classification of thematic roles in natural language texts. We employ no external source of information but automatic parse trees of the input sentences. We use a few attribute-value features and tree kernel functions applied to specialized structured features. Different configurations of our thematic role labeling system took part in 2 tasks of the SemEval 2007 evaluation campaign, namely the closed tasks on semantic role labeling for the English and the Arabic languages. In this paper we present and discuss the system configuration that participated in the English semantic role labeling task and present new results obtained after the end of the evaluation campaign.

• Type: Conference
• Year: 2007
• Where: AIIA
• Authors: Daniele Pighin, Alessandro Moschitti
• Keywords: AIIA 2007, Artificial Intelligence, Evaluation Campaign, Semantic Role Labeling, Thematic Role