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	Improving microblog retrieval with feedback entity model
	Fan, Feifan ; Qiang, Runwei ; Lv, Chao ; Yang, Jianwu
	2015
英文摘要	When searching over the microblogging, users prefer using queries including terms that represent some specific entities. Meanwhile, tweets, though limited within 140 characters, are often generated with one or more entities. Entities, as an important part of tweets, usually convey rich information for modeling relevance from new perspectives. In this paper, we propose a feedback entity model and integrate it into an adaptive language modeling framework in order to improve the retrieval performance. The feedback entity model is estimated with the latest entity-associated tweets based upon a regularized maximum likelihood criterion. More specifically, we assume that the entity-associated tweets are generated by a mixture model, which consists of the entity model, the domain-specific language model and the collection language model. Experimental results on two public Text Retrieval Conference (TREC) Twitter corpora demonstrate the significant superiority of our approach over the state-of-the-art baselines. ? 2015 ACM.; EI; 573-582; 19-23-Oct-2015
语种	中文
出处	24th ACM International Conference on Information and Knowledge Management, CIKM 2015
DOI标识	10.1145/2806416.2806461
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436558]
专题	计算机科学技术研究所
推荐引用方式 GB/T 7714	Fan, Feifan,Qiang, Runwei,Lv, Chao,et al. Improving microblog retrieval with feedback entity model. 2015-01-01.

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