Difference between revisions of "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. | + | '''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 == | == 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. | + | 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. |
Revision as of 11:30, 16 September 2019
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.
