Framework of Semantic Role Assignment based on Extended Lexical Conceptual Structure: Comparison with VerbNet and FrameNet

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 686–695, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics Framework of Semantic Role Assignment based on Extended Lexical Conceptual Structure: Comparison with VerbNet and FrameNet Yuichiroh Matsubayashi† Yusuke Miyao† Akiko Aizawa† †, National Institute of Informatics, Japan {y-matsu,yusuke,aizawa}@nii.ac.jp Abstract Widely accepted resources for semantic parsing, such as PropBank and FrameNet, are not perfect as a semantic role label- ing framework. Their semantic roles are not strictly defined; therefore, their mean- ings and semantic characteristics are un- clear. In addition, it is presupposed that a single semantic role is assigned to each syntactic argument. This is not necessarily true when we consider internal structures of verb semantics. We propose a new frame- work for semantic role annotation which solves these problems by extending the the- ory of lexical conceptual structure (LCS). By comparing our framework with that of existing resources, including VerbNet and FrameNet, we demonstrate that our ex- tended LCS framework can give a formal definition of semantic role labels, and that multiple roles of arguments can be repre- sented strictly and naturally. 1 Introduction Recent developments of large semantic resources have accelerated empirical research on seman- tic processing (M`arquez et al., 2008). Specif- ically, corpora with semantic role annotations, such as PropBank (Kingsbury and Palmer, 2002) and FrameNet (Ruppenhofer et al., 2006), are in- dispensable resources for semantic role labeling. However, there are two topics we have to carefully take into consideration regarding role assignment frameworks: (1) clarity of semantic role meanings and (2) the constraint that a single semantic role is assigned to each syntactic argument. While these resources are undoubtedly invalu- able for empirical research on semantic process- Sentence [John] threw [a ball] [from the window] . Affection Agent Patient Movement Source Theme Source/Path PropBank Arg0 Arg1 Arg2 VerbNet Agent Theme Source FrameNet Agent Theme Source Table 1: Examples of single role assignments with ex- isting resources. ing, current usage of semantic labels for SRL sys- tems is questionable from a theoretical viewpoint. For example, most of the works on SRL have used PropBank’s numerical role labels (Arg0 to Arg5). However, the meanings of these numbers depend on each verb in principle and PropBank does not expect semantic consistency, namely on Arg2 to Arg5. Moreover, Yi et al. (2007) explic- itly showed that Arg2 to Arg5 are semantically inconsistent. The reason why such labels have been used in SRL systems is that verb-specific roles generally have a small number of instances and are not suitable for learning. However, it is necessary to avoid using inconsistent labels since those labels confuse machine learners and can be a cause of low accuracy in automatic process- ing. In addition, clarity of the definition of roles are particularly important for users to rationally know how to use each role in their applications. For this reasons, well-organized and generalized labels grounded in linguistic characteristics are needed in practice. Semantic roles of FrameNet …

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