document: Learning from the past : answering new questions with past answers Community-based Question Answering sites , such as Yahoo ! Answers or Baidu Zhidao , allow users to get answers to complex , detailed and personal questions from other users . However , since answering a question depends on the ability and willingness of users to address the asker 's needs , a significant fraction of the questions remain unanswered . We measured that in Yahoo ! Answers , this fraction represents 15 % of all incoming English questions . At the same time , we discovered that around 25 % of questions in certain categories are recurrent , at least at the question-title level , over a period of one year . We attempt to reduce the rate of unanswered questions in Yahoo ! Answers by reusing the large repository of past resolved questions , openly available on the site . More specifically , we estimate the probability whether certain new questions can be satisfactorily answered by a best answer from the past , using a statistical model specifically trained for this task . We leverage concepts and methods from query-performance prediction and natural language processing in order to extract a wide range of features for our model . The key challenge here is to achieve a level of quality similar to the one provided by the best human answerers . We evaluated our algorithm on offline data extracted from Yahoo ! Answers , but more interestingly , also on online data by using three `` live '' answering robots that automatically provide past answers to new questions when a certain degree of confidence is reached . We report the success rate of these robots in three active Yahoo ! Answers categories in terms of both accuracy , coverage and askers ' satisfaction . This work presents a first attempt , to the best of our knowledge , of automatic question answering to questions of social nature , by reusing past answers of high quality .
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