Semantic Role Labeling of NomBank: A Maximum Entropy Approach

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), pages 138–145, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Semantic Role Labeling of NomBank: A Maximum Entropy Approach Zheng Ping Jiang and Hwee Tou Ng Department of Computer Science National University of Singapore 3 Science Drive 2, Singapore 117543 {jiangzp, nght}@comp.nus.edu.sg Abstract This paper describes our attempt NomBank-based automatic Semantic Roleat Labeling (SRL). NomBank is a project at New York University to annotate the ar- gument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classifica- tion problem and explore the possibility of adapting features previously shown use- ful in PropBank-based SRL systems. Var- ious NomBank-specific features are ex- plored. On test section 23, our best sys- tem achieves F1 score of 72.73 (69.14) when correct (automatic) syntactic parse trees are used. To our knowledge, this is the first reported automatic NomBank SRL system. 1 Introduction Automatic Semantic Role Labeling (SRL) sys- tems, made possible by the availability of Prop- Bank (Kingsbury and Palmer, 2003; Palmer et al., 2005), and encouraged by evaluation ef- forts in (Carreras and Marquez, 2005; Litkowski, 2004), have been shown to accurately determine the argument structure of verb predicates. A successful PropBank-based SRL system would correctly determine that “Ben Bernanke” is the subject (labeled as ARG0 in PropBank) of predicate “replace”, and “Greenspan” is the object (labeled as ARG1): • Ben Bernanke replaced Greenspan as Fed chair. • Greenspan was replaced by Ben Bernanke as Fed chair. The recent release of NomBank (Meyers et al., 2004c; Meyers et al., 2004b), a databank that an- notates argument structure for instances of com- mon nouns in the Penn Treebank II corpus, made it possible to develop automatic SRL systems that analyze the argument structures of noun predi- cates. Given the following two noun phrases and one sentence, a successful NomBank-based SRL sys- tem should label “Ben Bernanke” as the subject (ARG0) and “Greenspan” as the object (ARG1) of the noun predicate “replacement”. • Greenspan’s replacement Ben Bernanke • Ben Bernanke’s replacement of Greenspan • Ben Bernanke was nominated as Greenspan’s replacement. The ability to automatically analyze the argu- ment structures of verb and noun predicates would greatly facilitate NLP tasks like question answer- ing, information extraction, etc. This paper focuses on our efforts at building an accurate automatic NomBank-based SRL sys- tem. We study how techniques used in building PropBank SRL system can be transferred to de- veloping NomBank SRL system. We also make NomBank-specific enhancements to our baseline system. Our implemented SRL system and exper- iments are based on the September 2005 release of NomBank (NomBank.0.8). The rest of this paper is organized as follows: Section 2 gives an overview of NomBank, Sec- tion 3 introduces the Maximum Entropy classifica- tion model, Section 4 introduces our features and feature selection strategy, Section 5 explains the experimental setup and presents the experimen- tal results, Section 6 compares NomBank SRL to 138 S ������ …

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