Abstract | By combining role induction with a rule-based component for argument identification we obtain an unsupervised end-to-end semantic role labeling system. |
Experimental Setup | Although the dataset provides annotations for verbal and nominal predicate-argument constructions, we only considered the former, following previous work on semantic role labeling (Marquez et al., 2008). |
Experimental Setup | This baseline has been previously used as point of comparison by other unsupervised semantic role labeling systems (Grenager and Manning, 2006; Lang and Lapata, 2010) and shown difficult to outperform. |
Introduction | Indeed, the analysis produced by existing semantic role labelers has been shown to benefit a wide spectrum of applications ranging from information extraction (Surdeanu et al., 2003) and question answering (Shen and Lapata, 2007), to machine translation (Wu and Fung, 2009) and summarization (Melli et al., 2005). |
Introduction | Unfortunately, the reliance on role-annotated data which is expensive and time-consuming to produce for every language and domain, presents a major bottleneck to the widespread application of semantic role labeling . |
Introduction | In this paper we present a simple approach to unsupervised semantic role labeling . |
Learning Setting | We follow the general architecture of supervised semantic role labeling systems. |
Abstract | We present the results of evaluating translation utility by measuring the accuracy within a semantic role labeling (SRL) framework. |
Abstract | Finally, we show that replacing the human semantic role labelers with an automatic shallow semantic parser in our proposed metric yields an approximation that is about 80% as closely correlated with human judgment as HTER, at an even lower cost—and is still far better correlated than n-gram based evaluation metrics. |
Abstract | Role classification The agreement of classified roles is counted over the matching of the semantic role labels within two aligned word spans. |