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 - scientific work related to Wikipedia quality published in 2014, written by Shengnan Zhao, Bayar Tsolmon, Kyung-Soon Lee and Young-Seok Lee.

Overview

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, authors focus on extracting cross-lingual issue news from the Twitter data of Chinese and Korean. Authors 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 method is an effective for cross-lingual issue news detection.

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