Feature Analysis in Microblog Retrieval Based on Learning to Rank

摘要：

　　Learning to rank, which can fuse various of features, performs well in microblog retrieval.However, it is still unclear how the features function in microblog ranking.To address this issue, this paper examines the contribution of each single feature together with the contribution of the feature combinations via the ranking SVM for microblog retrieval modeling.The experimental re suits on the TREC microblog collection show that textual features, i.e.content relevance between a query and a microblog, contribute most to the retrieval per formance.And the combination of certain non-textual features and textual fea tures can further enhance the retrieval performance, though non-textual features alone produce rather weak results.

关键词： microblog retrieval learning to rank feature combination

作者: Zhongyuan Han Xuwei Li Muyun Yang Haoliang Qi Sheng Li

作者单位: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China ; School of School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China School of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, China

会议类型: 国际会议

会议名称: Second CCF Conference,NLPCC2013(第二届自然语言处理与中文计算会议)

会议地点: 重庆

会议语种:英文

页码: 410-416

在线出版日期: 2013-11-15（万方平台首次上网日期，不代表论文的发表时间）