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Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use of the FrameNet database but fail to show suitable generalization capabilities in out-of-domain scenarios. In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. The resulting argument classification model promotes a simpler feature space that limits the potential overfitting effects | Towards Open-Domain Semantic Role Labeling Danilo Croce Cristina Giannone Paolo Annesi Roberto Basili croce giannone annesi basili @info.uniroma2.it Department of Computer Science Systems and Production University of Roma Tor Vergata Abstract Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use of the FrameNet database but fail to show suitable generalization capabilities in out-of-domain scenarios. In this paper a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. The resulting argument classification model promotes a simpler feature space that limits the potential overfitting effects. The large scale empirical study here discussed confirms that state-of-art accuracy can be obtained for out-of-domain evaluations. 1 Introduction The availability of large scale semantic lexicons such as FrameNet Baker et al. 1998 allowed the adoption of a wide family of learning paradigms in the automation of semantic parsing. Building upon the so called frame semantic model Fillmore 1985 the Berkeley FrameNet project has developed a semantic lexicon for the core vocabulary of English since 1997. A frame is evoked in texts through the occurrence of its lexical units LU i.e. predicate words such verbs nouns or adjectives and specifies the participants and properties of the situation it describes the so called frame elements FEs . Semantic Role Labeling SRL is the task of automatic recognition of individual predicates together with their major roles e.g. frame elements as they are grammatically realized in input sentences. It has been a popular task since the availability of the PropBank and FrameNet annotated corpora Palmer et al. 2005 the seminal work of Gildea and Jurafsky 2002 and the successful CoNLL evaluation campaigns Carreras and Marquez 2005 . Statistical machine learning .
