Semantic Role Labeling via Tree Kernel Joint Inference

Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X), pages 61–68, New York City, June 2006. c⃝2006 Association for Computational Linguistics Semantic Role Labeling via Tree Kernel Joint Inference Alessandro Moschitti, Daniele Pighin and Roberto Basili Department of Computer Science University of Rome ”Tor Vergata” 00133 Rome, Italy {moschitti,basili}@info.uniroma2.it daniele.pighin@gmail.com Abstract Recent work on Semantic Role Labeling (SRL) has shown that to achieve high accuracy a joint inference on the whole predicate argument structure should be ap- plied. In this paper, we used syntactic sub- trees that span potential argument struc- tures of the target predicate in tree ker- nel functions. This allows Support Vec- tor Machines to discern between correct and incorrect predicate structures and to re-rank them based on the joint probabil- ity of their arguments. Experiments on the PropBank data show that both classifica- tion and re-ranking based on tree kernels can improve SRL systems. 1 Introduction Recent work on Semantic Role Labeling (SRL) (Carreras and M`arquez, 2005) has shown that to achieve high labeling accuracy a joint inference on the whole predicate argument structure should be applied. For this purpose, we need to extract fea- tures from the sentence’s syntactic parse tree that encodes the target semantic structure. This task is rather complex since we do not exactly know which are the syntactic clues that capture the relation be- tween the predicate and its arguments. For exam- ple, to detect the interesting context, the modeling of syntax/semantics-based features should take into account linguistic aspects like ancestor nodes or se- mantic dependencies (Toutanova et al., 2004). A viable approach to generate a large number of features has been proposed in (Collins and Duffy, 2002), where convolution kernels were used to im- plicitly define a tree substructure space. The selec- tion of the relevant structural features was left to the Voted Perceptron learning algorithm. Such success- ful experimentation shows that tree kernels are very promising for automatic feature engineering, espe- cially when the available knowledge about the phe- nomenon is limited. In a similar way, we can model SRL systems with tree kernels to generate large feature spaces. More in detail, most SRL systems split the labeling pro- cess into two different steps: Boundary Detection (i.e. to determine the text boundaries of predicate arguments) and Role Classification (i.e. labeling such arguments with a semantic role, e.g. Arg0 or Arg1 as defined in (Kingsbury and Palmer, 2002)). The former relates to the detection of syntactic parse tree nodes associated with constituents that corre- spond to arguments, whereas the latter considers the boundary nodes for the assignment of the suitable label. Both steps require the design and extraction of features from parse trees. As capturing the tightly interdependent relations among a predicate and its arguments is a complex task, we can apply tree ker- nels on the subtrees that span the whole predicate argument structure to generate the feature space of all the possible subtrees. In this paper, we apply the traditional bound- ary (TBC) and role (TRC) …

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