Framework of semantic role assignment based on extended lexical conceptual structure: comparison with VerbNet and FrameNet

Authors:
Yuichiroh Matsubayashi;Yusuke Miyao;Akiko Aizawa
Affiliations:
National Institute of Informatics, Japan;National Institute of Informatics, Japan;National Institute of Informatics, Japan
Venue:
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Year:
2012

Citing 4
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Class-Based Construction of a Verb Lexicon

Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Hybrid Natural Language Generation from Lexical Conceptual Structures

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Semantic role labeling: an introduction to the special issue

Computational Linguistics

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Abstract

Widely accepted resources for semantic parsing, such as PropBank and FrameNet, are not perfect as a semantic role labeling framework. Their semantic roles are not strictly defined; therefore, their meanings and semantic characteristics are unclear. 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 framework for semantic role annotation which solves these problems by extending the theory of lexical conceptual structure (LCS). By comparing our framework with that of existing resources, including VerbNet and FrameNet, we demonstrate that our extended LCS framework can give a formal definition of semantic role labels, and that multiple roles of arguments can be represented strictly and naturally.