Improving Chunk-based Semantic Role Labeling with Lexical Features

We present an approach for Semantic Role Labeling (SRL) using Conditional Random Fields in a joint identification/classification step. The approach is based on shallow syntactic information (chunks) and a number of lexicalized features such as selectional preferences and automatically inferred similar words, extracted using lexical databases and distributional similarity metrics. We use semantic annotations from the Proposition Bank for training and evaluate the system using CoNLL-2005 test sets. The additional lexical information led to improvements of 15% (in-domain evaluation) and 12% (out-of-domain evaluation) on overall semantic role classification in terms of F-measure. The gains come mostly from a better recall, which suggests that the addition of richer lexical information can improve the coverage of existing SRL models even when very little syntactic knowledge is available.