Retrieving Webpages Using Online Discussions

Kevin Ros, Matthew Jin, Jacob Levine, ChengXiang Zhai
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Abstract

Online discussions are a ubiquitous aspect of everyday life. An Internet user who interacts with an online discussion may benefit from seeing hyperlinks to webpages relevant to the discussion because the relevant webpages can provide added context, act as citations for background sources, or condense information so that conversations can proceed seamlessly at a high level. In this paper, we propose and study a new task of retrieving relevant webpages given an online discussion. We frame the task as a novel retrieval problem where we treat a sequence of comments in an online discussion as a query and use such a query to retrieve relevant webpages. We construct a new data set using Reddit, an online discussion forum, to study this new problem. We explore and evaluate multiple representative retrieval methods to examine their effectiveness for solving this new problem. We also propose to leverage the comments that contain hyperlinks as training data to enable supervised learning and further improve retrieval performance. We find that results using modern retrieval methods are promising and that leveraging comments with hyperlinks as training data can further improve performance. We release our data set and code to enable additional research in this direction.
使用在线讨论检索网页
在线讨论是日常生活中无处不在的一个方面。与在线讨论交互的Internet用户可能会从看到与讨论相关的网页的超链接中受益,因为相关的网页可以提供附加的上下文,作为背景资源的引用,或压缩信息,以便对话可以在高水平上无缝进行。本文提出并研究了一种基于在线讨论的相关网页检索的新任务。我们将该任务定义为一个新的检索问题,其中我们将在线讨论中的一系列评论视为查询,并使用该查询检索相关网页。我们使用在线讨论论坛Reddit构建了一个新的数据集来研究这个新问题。我们探索和评估了多种具有代表性的检索方法,以检验它们解决这一新问题的有效性。我们还建议利用包含超链接的评论作为训练数据,以实现监督学习,并进一步提高检索性能。我们发现使用现代检索方法的结果是有希望的,并且利用带有超链接的评论作为训练数据可以进一步提高性能。我们发布了我们的数据集和代码,以便在这个方向上进行更多的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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