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Will My Question Be Answered? Predicting 'Question Answerability' in Community Question-Answering Sites

机译:我的问题会得到回答吗?预测社区问题解答站点中的“问题回答能力”

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All askers who post questions in Community-based Question Answering (CQA) sites such as Yahoo! Answers, Quora or Baidu's Zhidao, expect to receive an answer, and are frustrated when their questions remain unanswered. We propose to provide a type of "heads up" to askers by predicting how many answers, if at all, they will get. Giving a preemptive warning to the asker at posting time should reduce the frustration effect and hopefully allow askers to rephrase their questions if needed. To the best of our knowledge, this is the first attempt to predict the actual number of answers, in addition to predicting whether the question will be answered or not. To this effect, we introduce a new prediction model, specifically tailored to hierarchically structured CQA sites. We conducted extensive experiments on a large corpus comprising 1 year of answering activity on Yahoo! Answers, as opposed to a single day in previous studies. These experiments show that the F_1 we achieved is 24% better than in previous work, mostly due the structure built into the novel model.
机译:在基于社区的问答(CQA)网站(例如Yahoo!)上发布问题的所有提问者。答案,Quora或百度的“智道”,都希望得到答案,而当他们的问题仍未得到解答时,他们会感到沮丧。我们建议通过预测会得到多少答案(如果有的话)来向提问者提供一种“提示”。在发帖时对发问者发出先发制人的警告应会减少沮丧感,并希望在需要时允许提问者重新陈述其问题。据我们所知,这是除了预测问题是否会得到回答之外,第一次尝试预测实际答案的数量。为此,我们引入了一种新的预测模型,专门针对分层结构的CQA网站进行了量身定制。我们对一个大型语料库进行了广泛的实验,包括对Yahoo!进行1年的回答活动。答案,而不是以前的研究中的一天。这些实验表明,我们获得的F_1比以前的工作提高了24%,这主要是由于新颖模型中内置了结构。

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