TAILIEUCHUNG - Báo cáo khoa học: "A Large-Scale Unified Lexical-Semantic Resource Based on LMF"

We present U BY, a large-scale lexicalsemantic 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 languages: English WordNet, Wiktionary, Wikipedia, FrameNet and VerbNet, German Wikipedia, Wiktionary and GermaNet, and multilingual OmegaWiki modeled according to the LMF standard. For FrameNet, VerbNet and all collaboratively 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. | 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 f Ubiquitous Knowledge Processing Lab UKP-DIPF German Institute for Educational Research and Educational Information t Ubiquitous Knowledge Processing Lab UKP-TUDA Department of Computer Science Technische Universitat Darmstadt http 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 languages English WordNet Wiktionary Wikipedia FrameNet and VerbNet German Wikipedia Wiktionary and GermaNet and multilingual OmegaWiki modeled according to the LMF standard. For FrameNet VerbNet and all collaboratively 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 foundation 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 neither by the largest single expert-constructed resources 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 independent efforts of combining existing LSRs to enhance their coverage . their breadth and depth . i the number of .

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