CAJ | 학술논문

从微博中准确而高效地检测突发事件是近年来的研究热点。在综合考虑词的突发性和其对文档覆盖度的基础上，提出突发词H指数的概念，用于抽取有效表征突发事件的突发特征。构造突发词-文档矩阵，结合凝聚式层次聚类算法对突发微博文本进行聚类，并借助微博原文对事件进行描述。在新浪微博数据集上的实验表明，该方法可以有效地检测出微博突发事件。
종미박중준학이고효지검측돌발사건시근년래적연구열점。재종합고필사적돌발성화기대문당복개도적기출상，제출돌발사H지수적개념，용우추취유효표정돌발사건적돌발특정。구조돌발사-문당구진，결합응취식층차취류산법대돌발미박문본진행취류，병차조미박원문대사건진행묘술。재신랑미박수거집상적실험표명，해방법가이유효지검측출미박돌발사건。
Mining emergencies accurately and efficiently from micro-blogs is one of the hottest issues in recent years. Based on the emer-gency of words and their coverage degree on documents, the concept of H-index of burst words was proposed to extract burst characters that could effectively represent the emergencies. By constructing the BurstWords-Document matrix, the Hierarchical Clustering Algorithm was used to cluster the burst documents. Based on the original micro-blog in each cluster, the emergencies were determined. Experiments on Sina Micro-blog showed that the approach proposed in this paper could effectively identify micro-blog emergencies.