A Cluster-Based Classification Approach to Semantic Role Labeling

In this paper, a new approach for multi-class classification problems is applied to the Semantic Role Labeling (SRL) problem, which is an important task for natural language processing systems to achieve better semantic understanding of text. The new approach applies to any classification problem with large feature sets. Data is partitioned using clusters on a subset of the features. A multi-label classifier is then trained individually on each cluster, using automatic feature selection to customize the larger feature set for the cluster. This algorithm is applied to the Semantic Role Labeling problem and achieves improvements in accuracy for both the argument identification classifier and the argument labeling classifier.