VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 627–637, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 627 VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling Andrea Di Fabio♦♥, Simone Conia♦, Roberto Navigli♦ ♦Department of Computer Science ♥Department of Literature and Modern Cultures Sapienza University of Rome, Italy {difabio,conia,navigli}@di.uniroma1.it Abstract We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from Word- Net into semantically-coherent frames. The frames define a common, prototypical argu- ment structure while at the same time pro- viding new concept-specific information. In contrast to PropBank, which defines enumer- ative semantic roles, VerbAtlas comes with an explicit, cross-frame set of semantic roles linked to selectional preferences expressed in terms of WordNet synsets, and is the first resource enriched with semantic information about implicit, shadow, and default arguments. We demonstrate the effectiveness of VerbAtlas in the task of dependency-based Semantic Role Labeling and show how its integration into a high-performance system leads to im- provements on both the in-domain and out-of- domain test sets of CoNLL-2009. VerbAtlas is available at http://verbatlas.org. 1 Introduction During the last two decades, we have witnessed increased attention to Natural Language Under- standing, a core goal of Natural Language Pro- cessing (NLP). Several challenges, however, are yet to be overcome when it comes to performing sentence-level semantic tasks (Navigli, 2018). In order to understand the meaning of sentences, the semantics of verbs plays a crucial role, since verbs define the argument structure roughly in terms of "who" did "what" to "whom", with the arguments being the constituents that bear a semantic relation (called semantic role) with the verb. In the fol- lowing example, "Joe" and "lunch" are arguments of "eat", whose argument structure identifies them, respectively, as the Agent and the Patient of the scenario evoked by the verb: [Joe]Agent is [eating]Verb his [lunch]Patient The automatic identification and labeling of ar- gument structures is a task pioneered by Gildea and Jurafsky (2002) called Semantic Role Label- ing (SRL). SRL has become very popular thanks to its integration into other related NLP tasks such as machine translation (Liu and Gildea, 2010), visual semantic role labeling (Silberer and Pinkal, 2018) and information extraction (Bas- tianelli et al., 2013). In order to be performed, SRL requires the fol- lowing core elements: 1) a verb inventory, and 2) a semantic role inventory. However, the current verb inventories used for this task, such as Prop- Bank (Palmer et al., 2005) and FrameNet (Baker et al., 1998), are language-specific and lack high- quality interoperability with existing knowledge bases. Furthermore, such resources provide low to medium coverage of the verbal lexicon (cf. Ta- ble 1), with PropBank showing the best figures, but still lower than other lexical inventories like WordNet (Fellbaum et al., 1998). Finally, the informativeness of the semantic roles defined in the various resources ranges from underspecified, …

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