Won Chul Kim

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Current user interface builders provide only low-level assistance, because they have knowledge of neither the application, nor the principles by which interface elements are combined effectively. We have developed a framework that unites the knowledge components essential for effective user interface presentation design. The framework consists of an(More)
Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have(More)
Automatic information extraction techniques such as named entity recognition and relation extraction have been developed but it is yet rare to apply them to various document types. In this paper, we applied them to academic literature and social media's contents in the field of diabetes to find distinctions between the perceptions of biomedical experts and(More)
The biomedical information extraction, especially Named Entity Recognition (NER), is a primary task in biomedical text-mining due to the rapid growth of large-scale literature. Extracting biomedical entities aims at identifying specific entities (words or phrases) from those unstructured text data. In this work, we introduce a novel biomedical NER system(More)
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