有趣的事实:自动琐事事实提取从维基百科

David Tsurel, D. Pelleg, Ido Guy, Dafna Shahaf
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引用次数: 24

摘要

很大一部分的网络搜索查询直接指向命名实体。搜索引擎探索各种方法来改善此类查询的用户体验。我们建议用关于被搜索实体的琐事来增强搜索结果。《琐事问答》在世界各地都很流行,并且能够提高用户粘性和留存率。大多数随机事实都不适合放在琐事部分。策划好的琐事需要技巧(和艺术)。在本文中,我们形式化了琐事价值的概念,并提出了一种从维基百科中自动挖掘琐事事实的算法。我们利用维基百科的分类结构,并根据它们的琐事质量对实体的类别进行排名。我们的算法能够发现有趣的事实,比如奥巴马获得格莱美奖,或者猫王当过坦克炮手。在用户研究中,我们的算法捕捉到的“好琐事”的直观概念比以前的工作高45%。搜索页面测试显示跳出率下降了22%,停留时间增加了12%,证明我们的事实吸引了用户的注意力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fun Facts: Automatic Trivia Fact Extraction from Wikipedia
A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of "good trivia" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention.
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