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Meta Search Models for Online Forum Thread Retrieval Research in Progress

机译:用于在线论坛话题检索的元搜索模型正在进行中

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Online forum thread retrieval is the task of retrieving threads that satisfy a user information need. Several thread representations have been proposed, and it has been found that combining these representations outperformed the retrieval using the individual representations. However, these combining methods leverage query relevance judgments to rank threads. Furthermore, in online forums, obtaining relevance judgments is not an option. As a result, in this paper, we propose to combine various thread representations using meta search techniques; many meta search techniques do not require training and has been found to produce a competitive result to the approaches that use relevance judgments. Our experimental result shows two things. First, combining thread representations using meta search techniques is an effective approach. Second, the CombSUM or the CombMNZ meta search techniques outperformed the best baseline method on high precision searches.
机译:在线论坛线程检索是检索满足用户信息需求的线程的任务。已经提出了几种线程表示,并且已经发现组合这些表示优于使用单独表示的检索。但是,这些组合方法利用查询相关性判断来对线程进行排名。此外,在在线论坛中,获得相关性判断不是一种选择。因此,在本文中,我们建议使用元搜索技术来组合各种线程表示形式。许多元搜索技术不需要培训,并且已经发现可以对使用相关性判断的方法产生竞争性的结果。我们的实验结果表明两件事。首先,使用元搜索技术组合线程表示是一种有效的方法。其次,在高精度搜索上,CombSUM或CombMNZ元搜索技术的性能优于最佳基准方法。

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