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|Abstract=DBpedia, as a large repository of structured knowledge, has become a valuable resource for language technologies. On the other hand, multilinguality and cross-linguality have emerged as issues of major interest. However, the deficiency in unstructured resources and cross-lingual structures for DBpedia hinders its more wide-spread application in natural language processing (NLP) tasks, especially in the cross-lingual context. In this paper, we introduce our NLP resources extracted from Wikipedia, which aim at bridging the gap between cross-lingual NLP and DBpedia. In order to achieve this, we exploited various kinds of elements and structures in Wikipedia to derive different associations between NLP elements and DBpedia resources.
 
|Abstract=DBpedia, as a large repository of structured knowledge, has become a valuable resource for language technologies. On the other hand, multilinguality and cross-linguality have emerged as issues of major interest. However, the deficiency in unstructured resources and cross-lingual structures for DBpedia hinders its more wide-spread application in natural language processing (NLP) tasks, especially in the cross-lingual context. In this paper, we introduce our NLP resources extracted from Wikipedia, which aim at bridging the gap between cross-lingual NLP and DBpedia. In order to achieve this, we exploited various kinds of elements and structures in Wikipedia to derive different associations between NLP elements and DBpedia resources.
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|Projekt=XLiMe, XLike
 
|Projekt=XLiMe, XLike
 
|Forschungsgruppe=Wissensmanagement
 
|Forschungsgruppe=Wissensmanagement
 
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Version vom 27. Oktober 2014, 15:57 Uhr


Bridging the Gap between Cross-lingual NLP and DBpedia by Exploiting Wikipedia


Bridging the Gap between Cross-lingual NLP and DBpedia by Exploiting Wikipedia



Published: 2014 Oktober

Buchtitel: Proceedings of the NLP&DBpedia workshop co-located with the 13th International Semantic Web Conference (ISWC 2014)
Verlag: CEUR-WS

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DBpedia, as a large repository of structured knowledge, has become a valuable resource for language technologies. On the other hand, multilinguality and cross-linguality have emerged as issues of major interest. However, the deficiency in unstructured resources and cross-lingual structures for DBpedia hinders its more wide-spread application in natural language processing (NLP) tasks, especially in the cross-lingual context. In this paper, we introduce our NLP resources extracted from Wikipedia, which aim at bridging the gap between cross-lingual NLP and DBpedia. In order to achieve this, we exploited various kinds of elements and structures in Wikipedia to derive different associations between NLP elements and DBpedia resources.

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