计算机科学 ›› 2014, Vol. 41 ›› Issue (5): 111-115.doi: 10.11896/j.issn.1002-137X.2014.05.024
梁超,吕钊,顾君忠
LIANG Chao,LV Zhao and GU Jun-zhong
摘要: 随着互联网的不断发展,用户因不能准确输入查询关键字而无法准确获取未知领域信息的问题日益严重。作为一种根据已知领域知识获取未知领域知识的全新检索方式,类比检索逐渐成为研究热点。类比检索通过分析词对之间的潜在关系而准确地返回目标信息。例如,给定类比查询请求Q={A:B,C:?},A与B之间具有某种潜在关系,类比检索的目标是得到?所代表的目标词(集)D,其中A与B的关系和C与D的潜在关系相似。类比检索的两个难点是潜在关系挖掘和目标词抽取,这两个问题对于中文而言,更具挑战性。提出了基于SVM的中文类比检索方法(SVM based Chinese Analogy Retrieval,SVMbCAR)。该方法的两个主要成分包括基于SVM的关系代表词抽取和目标词确定。基于真实测试数据集(包含源自人立方的600个人物实体对)的实验表明,SVMbCAR方法抽取关系代表词的准确率为82.3%,抽取目标词的准确率为90.5%。
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