UBY - A Large-Scale Unified Lexical-Semantic Resource Based on LMF

Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pages 580–590, Avignon, France, April 23 - 27 2012. c⃝2012 Association for Computational Linguistics UBY – A Large-Scale Unified Lexical-Semantic Resource Based on LMF Iryna Gurevych†‡, Judith Eckle-Kohler‡, Silvana Hartmann‡, Michael Matuschek‡, Christian M. Meyer‡ and Christian Wirth‡ † Ubiquitous Knowledge Processing Lab (UKP-DIPF) German Institute for Educational Research and Educational Information ‡ Ubiquitous Knowledge Processing Lab (UKP-TUDA) Department of Computer Science Technische Universit¨at Darmstadt http://www.ukp.tu-darmstadt.de Abstract We present UBY, a large-scale lexical- semantic resource combining a wide range of information from expert-constructed and collaboratively constructed resources for English and German. It currently contains nine resources in two lan- guages: Wikipedia,English WordNet, Wiktionary, FrameNet and VerbNet, German Wikipedia, Wiktionary and GermaNet, and multilingual OmegaWiki modeled according to the LMF standard. For FrameNet, VerbNet and all collabora- tively constructed resources, this is done for the first time. Our LMF model captures lexical information at a fine-grained level by employing a large number of Data Categories from ISOCat and is designed to be directly extensible by new languages and resources. All resources in UBY can be accessed with an easy to use publicly available API. 1 Introduction Lexical-semantic resources (LSRs) are the foun- dation of many NLP tasks such as word sense disambiguation, semantic role labeling, question answering and information extraction. They are needed on a large scale in different languages. The growing demand for resources is met nei- ther by the largest single expert-constructed re- sources (ECRs), such as WordNet and FrameNet, whose coverage is limited, nor by collaboratively constructed resources (CCRs), such as Wikipedia and Wiktionary, which encode lexical-semantic knowledge in a less systematic form than ECRs, because they are lacking expert supervision. Previously, there have been several indepen- dent efforts of combining existing LSRs to en- hance their coverage w.r.t. their breadth and depth, i.e. (i) the number of lexical items, and (ii) the types of lexical-semantic information contained (Shi and Mihalcea, 2005; Johansson and Nugues, 2007; Navigli and Ponzetto, 2010b; Meyer and Gurevych, 2011). As these efforts often targeted particular applications, they focused on aligning selected, specialized information types. To our knowledge, no single work focused on modeling a wide range of ECRs and CCRs in multiple lan- guages and a large variety of information types in a standardized format. Frequently, the presented model is not easily scalable to accommodate an open set of LSRs in multiple languages and the in- formation mined automatically from corpora. The previous work also lacked the aspects of lexicon format standardization and API access. We be- lieve that easy access to information in LSRs is crucial in terms of their acceptance and broad ap- plicability in NLP. In this paper, we propose a solution to this. We define a standardized format for modeling LSRs. This is a prerequisite for resource interoperabil- ity and the smooth integration of resources. We employ the ISO standard Lexical Markup Frame- work (LMF: ISO 24613:2008), a metamodel for LSRs (Francopoulo et al., …

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