Semantic Role Labeling using Linear-Chain CRF

The aim of this paper is to present a simplified take on applying linear-chain conditional random fields (CRF) to semantic role labeling (SRL), with a focus on German. The dataset is adapted from the semantic parsing track of the CoNLL-2009 shared task on syntactic and semantic dependencies in multiple languages. By treating SRL as a sequence labeling task, the framework architecture becomes very simple. Building on a set of hand-crafted features, a linear-chain CRF model is trained which jointly performs argument identification and classification in a single step. The best results on the sequence tagging task are obtained by the model which integrates basic argument and predicate features, as well as a binary feature indicating if a given argument is a syntactic child of the predicate in the dependency tree. We found that for our system, employing more distinct features on syntactic dependents of the predicate impaired model performance.