Semantic Role Labeling via Instance-Based Learning

This paper demonstrates two methods to improve the performance of instancebased 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 CoNLL2005 Shared Task. It is shown that applying the Tree-Based PredicateArgument Recognition Algorithm (PARA) to the data as a preprocessing stage allows kNN and PML to deliver F1:

• Type: Conference
• Year: 2006
• Where: EMNLP
• Authors: Chi-san Althon Lin, Tony C. Smith
• Keywords: EMNLP 2006, EMNLP 2007, Priority Maximum Likelihood, Semantic Role Labeling, Stage Allows KNN