Hierarchical Recognition of Propositional Arguments with Perceptrons

Abstract

We describe a system for the CoNLL-2004 Shared Task on Semantic Role Labeling (Carreras and Màrquez, 2004a). The system implements a two-layer learning architecture to recognize arguments in a sentence and predict the role they play in the propositions. The exploration strategy visits possible arguments bottom-up, navigating through the clause hierarchy. The learning components in the architecture are implemented as Perceptrons, and are trained simultaneously online, adapting their behavior to the global target of the system. The learning algorithm follows the global strategy introduced in (Collins, 2002) and adapted in (Carreras and Màrquez, 2004b) for partial parsing tasks.

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Cite this paper

@inproceedings{Carreras2004HierarchicalRO, title={Hierarchical Recognition of Propositional Arguments with Perceptrons}, author={Xavier Carreras and Llu{\'i}s M{\`a}rquez i Villodre and Grzegorz Chrupala}, booktitle={CoNLL}, year={2004} }