Chinese and Korean cross-lingual issue news detection based on translation knowledge of Wikipedia

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Chinese and Korean cross-lingual issue news detection based on translation knowledge of Wikipedia is a 2014 conference paper written in English by Zhao S., Tsolmon B., Lee K.-S., Lee Y.-S. and published in Lecture Notes in Electrical Engineering.

[edit] Abstract

Cross-lingual issue news and analyzing the news content is an important and challenging task. The core of the cross-lingual research is the process of translation. In this paper, we focus on extracting cross-lingual issue news from the Twitter data of Chinese and Korean. We propose translation knowledge method for Wikipedia concepts as well as the Chinese and Korean cross-lingual inter-Wikipedia link relations. The relevance relations are extracted from the category and the page title of Wikipedia. The evaluation achieved a performance of 83% in average precision in the top 10 extracted issue news. The result indicates that our method is an effective for cross-lingual issue news detection.

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