Paper: Semantic Role Labeling Via Instance-Based Learning

ACL ID W06-1622
Title Semantic Role Labeling Via Instance-Based Learning
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

This paper demonstrates two methods to improve the performance of instance- based learning (IBL) algorithms for the problem of Semantic Role Labeling (SRL). Two IBL algorithms are utilized: k-Nearest Neighbor (kNN), and Priority Maximum Likelihood (PML) with a modified back-off combination method. The experimental data are the WSJ23 and Brown Corpus test sets from the CoNLL- 2005 Shared Task. It is shown that ap- plying the Tree-Based Predicate- Argument Recognition Algorithm (PARA) to the data as a preprocessing stage allows kNN and PML to deliver F1: 68.61 and 71.02 respectively on the WSJ23, and F1: 56.96 and 60.55 on the Brown Corpus; an increase of 8.28 in F1 measurement over the most recent pub- lished PML results for this problem (Palmer et al. , 2005). Training times for IBL algori...