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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