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Targeted question answering on smartphones utilizing app based user classification

机译:利用基于应用程序的用户分类在智能手机上进行有针对性的问题解答

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State-of-the-art question answering systems are pretty successful on well-formed factual questions, however they fail on the non-factual ones. In order to investigate effective algorithms for answering non-factual questions, we deployed a crowdsourced multiple choice question answering system for playing “Who wants to be a millionaire?” game. To build a crowdsourced super-player for “Who wants to be a millionaire?”, we propose an app based user classification approach. We identify the target user groups for a multiple choice question based on the apps installed on their smartphones. Our final algorithm improves the answering accuracy by 10% on overall, and by 35% on harder questions compared to the majority voting. Our results pave the way to build highly accurate crowdsourced question answering systems.
机译:最先进的问答系统在格式正确的事实问题上非常成功,但在非事实问题上却不可行。为了研究用于回答非事实性问题的有效算法,我们部署了众包的多项选择问题解答系统来播放“谁想成为百万富翁?”游戏。为了构建“谁想成为百万富翁?”的众包超级玩家,我们提出了一种基于应用程序的用户分类方法。我们根据智能手机上安装的应用为多个选择题确定目标用户组。与多数投票相比,我们的最终算法将整体的回答准确性提高了10%,对较难回答的问题的回答准确性提高了35%。我们的结果为构建高度精确的众包问答系统铺平了道路。

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