Short Text Similarity Measurement Based on Coupled Semantic Relation and Strong Classification Features

摘要：

　　Measuring the similarity between short texts is made difficult by the fact that two texts that are semantically related may not contain any words in common.In this paper,we propose a novel short text similarity measure which aggregates coupled semantic relation(CSR)and strong classification features(SCF)to provide a richer semantic context.On the one hand,CSR considers both intra-relation(i.e.cooccurrence of terms based on the modified weighting strategy)and interrelation(i.e.dependency of terms via paths that connect linking terms)between a pair of terms.On the other hand,Based on SCF for similarity measure is established based on the idea that the more similar two texts are,the more features of strong classification they share.Finally,we combine the above two techniques to address the semantic sparseness of short text.We carry out extensive experiments on real world short texts.The results demonstrate that our method significantly outperforms baseline methods on several evaluation metrics.

关键词： Short text Coupled semantic relation Strong classification feature Short text similarity

作者: Huifang Ma Wen Liu Zhixin Li Xianghong Lin

作者单位: College of Computer Science and Technology,Northwest Normal University,Lanzhou 730000,China;Guangxi College of Computer Science and Technology,Northwest Normal University,Lanzhou 730000,China Guangxi Key Laboratory of Multi-source Information Mining and Security,Guangxi Normal University,Gui

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 135-147

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）