Improving Chunk-based Semantic Role Labeling with Lexical Features

We present an approach for Seman-tic Role Labeling (SRL) using Condi-tional Random Fields in a joint identifi-cation/classification step. The approach is based on shallow syntactic information (chunks) and a number of lexicalized fea-tures such as selectional preferences and automatically inferred similar words, ex-tracted using lexical databases and distri-butional similarity metrics. We use se-mantic annotations from the Proposition Bank for training and evaluate the system using CoNLL-2005 test sets. The addi-tional lexical information led to improve-ments of 15 % (in-domain evaluation) and 12 % (out-of-domain evaluation) on over-all semantic role classification in terms of F-measure. The gains come mostly from a better recall, which suggests that the addi-tion of richer lexical information can im-prove the coverage of existing SRL mod-els even when very little syntactic knowl-edge is available.