Towards Open-Domain Semantic Role Labeling

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 237–246, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics 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 technolo- gies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based sys- tems currently make use of the FrameNet database but fail to show suitable general- ization capabilities in out-of-domain sce- narios. 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 argu- ment classification model promotes a sim- pler feature space that limits the potential overfitting effects. The large scale em- pirical 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 (Fill- more, 1985), the Berkeley FrameNet project has developed a semantic lexicon for the core vocab- ulary 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 prop- erties of the situation it describes, the so called frame elements (FEs). Semantic Role Labeling (SRL) is the task of automatic recognition of individual predicates to- gether with their major roles (e.g. frame ele- ments) as they are grammatically realized in in- put sentences. It has been a popular task since the availability of the PropBank and FrameNet an- notated corpora (Palmer et al., 2005), the seminal work of (Gildea and Jurafsky, 2002) and the suc- cessful CoNLL evaluation campaigns (Carreras and M`arquez, 2005). Statistical machine learning methods, ranging from joint probabilistic models to support vector machines, have been success- fully adopted to provide very accurate semantic labeling, e.g. (Carreras and M`arquez, 2005). SRL based on FrameNet is thus not a novel task, although very few systems are known capable of completing a general frame-based annotation pro- cess over raw texts, noticeable exceptions being discussed for example in (Erk and Pado, 2006), (Johansson and Nugues, 2008b) and (Coppola et al., 2009). Some critical limitations have been out- lined in literature, some of them independent from the underlying semantic paradigm. Parsing Accuracy. Most of the employed learning algorithms are based on complex sets of syntagmatic features, as deeply investigated in (Jo- hansson and Nugues, 2008b). The resulting recog- nition is thus highly dependent on the accuracy of the underlying parser, whereas wrong structures returned by the parser usually imply large misclas- sification errors. Annotation costs. Statistical learning ap- proaches applied to SRL are very demanding with respect to the amount and quality of the train- ing material. The complex SRL architectures proposed (usually …

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