Semantic Role Chunking Combining Complementary Syntactic Views

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 217–220, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics Semantic Role Chunking Combining Complementary Syntactic Views Sameer Pradhan, Kadri Hacioglu, Wayne Ward, James H. Martin and Daniel Jurafsky† Center for Spoken Language Research, University of Colorado, Boulder, CO 80303 †Department of Linguistics, Stanford University, Stanford, CA 94305 {spradhan,hacioglu,whw,martin}@cslr.colorado.edu, jurafsky@stanford.edu Abstract This paper describes a semantic role la- beling system that uses features derived from different syntactic views, and com- bines them within a phrase-based chunk- ing paradigm. For an input sentence, syn- tactic constituent structure parses are gen- erated by a Charniak parser and a Collins parser. Semantic role labels are assigned to the constituents of each parse using Support Vector Machine classifiers. The resulting semantic role labels are con- verted to an IOB representation. These IOB representations are used as additional features, along with flat syntactic chunks, by a chunking SVM classifier that pro- duces the final SRL output. This strategy for combining features from three differ- ent syntactic views gives a significant im- provement in performance over roles pro- duced by using any one of the syntactic views individually. 1 Introduction The task of Semantic Role Labeling (SRL) involves tagging groups of words in a sentence with the se- mantic roles that they play with respect to a particu- lar predicate in that sentence. Our approach is to use supervised machine learning classifiers to produce the role labels based on features extracted from the input. This approach is neutral to the particular set of labels used, and will learn to tag input according to the annotated data that it is trained on. The task reported on here is to produce PropBank (Kingsbury and Palmer, 2002) labels, given the features pro- vided for the CoNLL-2005 closed task (Carreras and M`arquez, 2005). We have previously reported on using SVM clas- sifiers for semantic role labeling. In this work, we formulate the semantic labeling problem as a multi- class classification problem using Support Vector Machine (SVM) classifiers. Some of these systems use features based on syntactic constituents pro- duced by a Charniak parser (Pradhan et al., 2003; Pradhan et al., 2004) and others use only a flat syn- tactic representation produced by a syntactic chun- ker (Hacioglu et al., 2003; Hacioglu and Ward, 2003; Hacioglu, 2004; Hacioglu et al., 2004). The latter approach lacks the information provided by the hierarchical syntactic structure, and the former imposes a limitation that the possible candidate roles should be one of the nodes already present in the syntax tree. We found that, while the chunk based systems are very efficient and robust, the systems that use features based on full syntactic parses are generally more accurate. Analysis of the source of errors for the parse constituent based systems showed that incorrect parses were a major source of error. The syntactic parser did not produce any constituent that corresponded to the correct segmen- tation for the semantic argument. In Pradhan et al. (2005), …

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