Young-rok Cha

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Query result clustering has attracted considerable attention as a means of providing users with a concise overview of results. However, little research effort has been devoted to organizing the query results for entities which refer to real-world concepts, e.g., people, products, and locations. Entity-level result clustering is more challenging because(More)
This paper studies named entity translation and proposes " selective temporal-ity " as a new feature, as using temporal features may be harmful for translating " atemporal " entities. Our key contribution is building an automatic classifier to distinguish temporal and atemporal entities then align them in separate procedures to boost translation accuracy by(More)
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