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Latent SVMs for Semantic Role Labeling using LTAG Derivation Trees ∗

By Yudong Liu, Gholamreza Haffari and Anoop Sarkar


A phrase structure parse tree for a sentence can be generated by many different Lexicalized Tree-Adjoining Grammar (LTAG) derivation trees. In this paper, we use multiple LTAG derivations as latent features for semantic role labeling (SRL). We hypothesize that positive and negative examples of individual semantic roles can be reliably distinguished by possibly different latent LTAG-based features. We use latent support vector machines (LSVM) for the SRL task using these latent LTAG features. Our experiments on the PropBank-CoNLL’2005 dataset show that our method obtains 89.59 % F1 score which is significantly better than the state of the art F1 score of 80.32 % on this task.

Year: 2011
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