Chinese semantic role labeling with shallow parsing

Abstract

Most existing systems for Chinese Semantic Role Labeling (SRL) make use of full syntactic parses. In this paper, we evaluate SRL methods that take partial parses as inputs. We first extend the study on Chinese shallow parsing presented in (Chen et al., 2006) by raising a set of additional features. On the basis of our shallow parser, we implement SRL systems which cast SRL as the classification of syntactic chunks with IOB2 representation for semantic roles (i.e. semantic chunks). Two labeling strategies are presented: 1) directly tagging semantic chunks in onestage, and 2) identifying argument boundaries as a chunking task and labeling their semantic types as a classification task. For both methods, we present encouraging results, achieving significant improvements over the best reported SRL performance in the literature. Additionally, we put forward a rule-based algorithm to automatically acquire Chinese verb formation, which is empirically shown to enhance SRL. ? 2009 ACL and AFNLP.