Runwei Qiang

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Information seeking behavior in microblogging environments such as Twitter differs from traditional web search. The best performing microblog retrieval techniques attempt to utilize both semantic and temporal aspects of documents. In this paper, we present an effective approach, including the query modeling, the document modeling and the temporal(More)
This paper describes our approaches to temporally-anchored ad hoc retrieval task and tweet timeline generation (TTG) task in the TREC 2014 Microblog track. In the ad hoc search, we apply a learning to rank framework which utilizes not only the various content relevance of a tweet, but also the quality of a tweet. External evidences are well incorporated in(More)
Learning to rank method has been proposed for practical application in the field of information retrieval. When employing it in microblog retrieval, the significant interactions of various involved features are rarely considered. In this paper, we propose a Ranking Factorization Machine (Ranking FM) model, which applies Factorization Machine model to(More)
This paper describes the PKUICST's entry into the TREC 2012 Microblog track. In this year of microblog track, we participate in both the Real-time Adhoc Task and Real-time Filtering Task. In the Real-time Adhoc Task, we designed and conducted a series of experiments based on different retrieval models, namely Real-time Tweet Ranking (RTR) model and learning(More)
Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for(More)
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,(More)
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