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Automatic extraction of hyponymy relations between concepts in an ontology is significant for ontology learning and knowledge organization. In this paper, we propose a fusion approach of hyponymy relation extraction in patent domain, using Relative Decoration Degree (RDEG) to extract high precision relations, and then Association Rule (AR) to enrich those relations. We use Cilin to extend a word to...
The bootstrapping method of recognizing names in query logs is useful in some ways, but it is affected by noisy data, which comes from irrelevant queries and irrelevant template matching. We propose a trend selection method which aims to select templates more relevant to personal names rather than other categories. In order to eliminate noisy data when matching, the boundaries of candidates are relocated...
Topic Detection is a sub-task of Topic Detection and Tracking, its main task is to find and organize topics that system didn't know. By analyzing hundreds of website news reports, we find that usually there exist some keywords in text, and early study didn't pay enough attention to this, we propose a topic detection algorithm according this. The algorithm is based on K-means clustering algorithm,...
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