Improving the extraction of bilingual terminology from Wikipedia
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Cross-lingual semantic relatedness using encyclopedic knowledge
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Building Bilingual Parallel Corpora Based on Wikipedia
ICCEA '10 Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 02
An approach for extracting bilingual terminology from Wikipedia
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
WikiTranslate: query translation for cross-lingual information retrieval using only Wikipedia
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
BabelNet: building a very large multilingual semantic network
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Untangling the cross-lingual link structure of Wikipedia
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
MENTA: inducing multilingual taxonomies from wikipedia
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Cross lingual text classification by mining multilingual topics from wikipedia
Proceedings of the fourth ACM international conference on Web search and data mining
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Wikipedia is a Web based, freely available multilingual encyclopedia, constructed in a collaborative effort by thousands of contributors. Wikipedia articles on the same topic in different languages are connected via interlingual (or translational) links. These links serve as an excellent resource for obtaining lexical translations, or building multilingual dictionaries and semantic networks. As these links are manually built, many links are missing or simply wrong. This paper describes a supervised learning method for generating new links and detecting existing incorrect links. Since there is no dataset available to evaluate the resulting interlingual links, we create our own gold standard by sampling translational links from four language pairs using distance heuristics. We manually annotate the sampled translation links and used them to evaluate the output of our method for automatic link detection and correction.