Kuan-Hsi Chen

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A number of authoritative medical websites, such as PubMed and MedlinePlus, provide consumers with the most up-to-date health information. However, non-English speakers often encounter not only language barriers (from other languages to English) but also terminology barriers (from laypersons inverted exclamation mark| terms to professional medical terms)(More)
Due to language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. A few cross-language medical information retrieval (CLMIR) systems have been utilizing MeSH (Medical Subject Heading) with multilingual thesaurus to bridge the gap.(More)
At the NTCIR-6 CLQA (Cross-Language Question Answering) task, we participated in the Chinese-Chinese (C-C) and English-Chinese (E-C) QA (Question Answering) subtasks. Without employing question type classification, we proposed a new resource, Wikipedia, to assist in extracting and ranking Question-Focused terms. We regarded the titles of Wikipedia articles(More)
Social networks have become a popular and powerful communication platform as the mobile technology evolves. To evaluate the influence of a message on a social network, response prediction is crucial in modeling the message propagation and interaction among users. To predict whether a new message will receive responses, we propose a two-stage learning method(More)
Undoubtedly friendship is one of key factors which keep social networking service users active and the whole community expanding. Hence, predicting friendships becomes an indispensable service provided by the platforms like Plurk, Twitter and Facebook. In this study, an empirical prediction resolution is presented by taking into account the interactions(More)
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