Kweri ME:一个基于问答的模型,用于预测CQA站点中可接受的问题答案

V. Chethana, Evlin Vidyu Latha P
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引用次数: 0

摘要

从社区问答(CQA)网站上针对给定问题发布的多个答案中找到最佳答案的方法,如果使用手动过程,则是乏味且最耗时的。专家们也需要分析所有给出的答案。为此,我们提出了一种基于主题建模和分类器的新方法。为了评估模型的正确性,使用了一组参数,如接收者操作特征曲线下面积、曲线下精确召回面积、混淆矩阵、F1分数和准确性。结果表明,该模型能够有效地预测最佳答案。
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Kweri ME: A Q&A based model which predicts the accepted answers of questions in CQA sites
An approach to find the best answers from the multiple answer posted for given question in Community Question Answering(CQA) sites is tedious and most time consuming if manual process used. And also the experts are required to do this by analysis all given answers. So we presented an new approach based on topic modeling and classifier. To evaluate correctness of the proposed model, a set of parameters are used, such as Receiver Operating Characteristics Area Under Curve, Precision Recall Area Under Curve, Confussion matrix, F1 score and Accuracy. Results show that the proposed model is effective in predicting the best answer.
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