Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Collaborative tagging has been widely adopted by social media bsites to allow users to describe content with metadata tags. Tagging can greatly improve search results.  propose a cross-language social media retrieval system (CLSMR) to help users retrieve foreign-language tagged media content.  construct a Chinese to Korean CLSMR system that translates Chinese queries into Korean, retrieves content, and then translates the Korean tags in the search results back into Chinese. Our system translates NEs using a dictionary of bilingual NE pairs from Wikipedia and a pattern-based software translator which learns regular NE patterns from the b. The top-10 precision of YouTube retrieved results for our system was 0.39875. The K-C NE tag translation accuracy for the top-10 YouTube results was 77.6%, which shows that our translation method is fairly effective for named entities. A questionnaire given to users shod that automatically translated tags re considered as informative as a human-written summary. With our proposed CLSMR system, Chinese users can retrieve online Korean media files and get a basic understanding of their content with no knowledge of the Korean language.
['Yu-Chun Wang', 'Jian-Ting Chen', 'Richard Tzong-Han Tsai', 'Wen-Lian Hsu']
I am writing a research paper on a idea Query and tag translation for Chinese-Korean cross-language social media retrieval
[{"from": "human", "value": "I am writing a research paper on a idea Query and tag translation for Chinese-Korean cross-language social media retrieval"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Collaborative tagging has been widely adopted by social media bsites to allow users to describe content with metadata tags. Tagging can greatly improve search results.  propose a cross-language social media retrieval system (CLSMR) to help users retrieve foreign-language tagged media content.  construct a Chinese to Korean CLSMR system that translates Chinese queries into Korean, retrieves content, and then translates the Korean tags in the search results back into Chinese. Our system translates NEs using a dictionary of bilingual NE pairs from Wikipedia and a pattern-based software translator which learns regular NE patterns from the b. The top-10 precision of YouTube retrieved results for our system was 0.39875. The K-C NE tag translation accuracy for the top-10 YouTube results was 77.6%, which shows that our translation method is fairly effective for named entities. A questionnaire given to users shod that automatically translated tags re considered as informative as a human-written summary. With our proposed CLSMR system, Chinese users can retrieve online Korean media files and get a basic understanding of their content with no knowledge of the Korean language."}]