A Comparative Study on Generalization of Semantic Roles in FrameNet

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 19–27, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP A Comparative Study on Generalization of Semantic Roles in FrameNet Yuichiroh Matsubayashi† Naoaki Okazaki† Jun’ichi Tsujii†‡∗ †Department of Computer Science, University of Tokyo, Japan ‡School of Computer Science, University of Manchester, UK ∗National Centre for Text Mining, UK {y-matsu,okazaki,tsujii}@is.s.u-tokyo.ac.jp Abstract A number of studies have presented machine-learning approaches to semantic role labeling with availability of corpora such as FrameNet and PropBank. These corpora define the semantic roles of predi- cates for each frame independently. Thus, it is crucial for the machine-learning ap- proach to generalize semantic roles across different frames, and to increase the size of training instances. This paper ex- plores several criteria for generalizing se- mantic roles in FrameNet: role hierar- chy, human-understandable descriptors of roles, semantic types of filler phrases, and mappings from FrameNet roles to the- matic roles of VerbNet. We also pro- pose feature functions that naturally com- bine and weight these criteria, based on the training data. The experimental result of the role classification shows 19.16% and 7.42% improvements in error reduc- tion rate and macro-averaged F1 score, re- spectively. We also provide in-depth anal- yses of the proposed criteria. 1 Introduction Semantic Role Labeling (SRL) is a task of analyz- ing predicate-argument structures in texts. More specifically, SRL identifies predicates and their arguments with appropriate semantic roles. Re- solving surface divergence of texts (e.g., voice of verbs and nominalizations) into unified seman- tic representations, SRL has attracted much at- tention from researchers into various NLP appli- cations including question answering (Narayanan and Harabagiu, 2004; Shen and Lapata, 2007; buy.v PropBank FrameNet Frame buy.01 Commerce buy Roles ARG0: buyer Buyer ARG1: thing bought Goods ARG2: seller Seller ARG3: paid Money ARG4: benefactive Recipient ... ... Figure 1: A comparison of frames for buy.v de- fined in PropBank and FrameNet Moschitti et al., 2007), and information extrac- tion (Surdeanu et al., 2003). In recent years, with the wide availability of cor- pora such as PropBank (Palmer et al., 2005) and FrameNet (Baker et al., 1998), a number of stud- ies have presented statistical approaches to SRL (M`arquez et al., 2008). Figure 1 shows an exam- ple of the frame definitions for a verb buy in Prop- Bank and FrameNet. These corpora define a large number of frames and define the semantic roles for each frame independently. This fact is problem- atic in terms of the performance of the machine- learning approach, because these definitions pro- duce many roles that have few training instances. PropBank defines a frame for each sense of predicates (e.g., buy.01), and semantic roles are defined in a frame-specific manner (e.g., buyer and seller for buy.01). In addition, these roles are asso- ciated with tags such as ARG0-5 and AM-*, which are commonly used in different frames. Most SRL studies on PropBank have used these tags in order to gather a sufficient amount of training …

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