array(35) { ["id"]=> string(4) "2048" ["type"]=> string(6) "course" ["title"]=> string(42) "基于双向RNN的保险条款问答系统" ["subtitle"]=> string(0) "" ["creator"]=> array(6) { ["id"]=> string(1) "3" ["nickname"]=> string(5) "admin" ["title"]=> string(1) " " ["uuid"]=> string(40) "634d3b58166bfafd4069119be97ee6bfee064c52" ["destroyed"]=> string(1) "0" ["avatar"]=> array(3) { ["small"]=> string(75) "http://www.chinadatacase.com/files/user/otherform/1711697442_1722994936.png" ["middle"]=> string(75) "http://www.chinadatacase.com/files/user/otherform/1711697442_1722994936.png" ["large"]=> string(75) "http://www.chinadatacase.com/files/user/otherform/1711697442_1722994936.png" } } ["showable"]=> string(1) "1" ["buyable"]=> string(1) "1" ["summary"]=> string(728) "

如今，保险业蓬勃发展，但大量繁杂的保险条款给投保人在选择时带来了困难，因此，急需一个基于保险条款的智能问答系统，帮助投保人更好地理解保险条款的内容。本案例提供了一个基于双向RNN的智能问答系统的构建过程，手把手地带领学生从数据下载、编码与解码、文本匹配等过程中构建并亲自动手训练一个智能问答系统。最终结果表明，问答系统的预测答案的与真实答案的平均余弦相似度为0.9636，这说明该问答系统能较好地理解保险条款的信息。此外，本文还探讨了模型在未来的优化方向，如加入术语词典和选择不同的模型架构。

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如今，保险业蓬勃发展，但大量繁杂的保险条款给投保人在选择时带来了困难，因此，急需一个基于保险条款的智能问答系统，帮助投保人更好地理解保险条款的内容。本案例提供了一个基于双向RNN的智能问答系统的构建过程，手把手地带领学生从数据下载、编码与解码、文本匹配等过程中构建并亲自动手训练一个智能问答系统。最终结果表明，问答系统的预测答案的与真实答案的平均余弦相似度为0.9636，这说明该问答系统能较好地理解保险条款的信息。此外，本文还探讨了模型在未来的优化方向，如加入术语词典和选择不同的模型架构。

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中国经管实验教学案例平台

www.chinadatacase.com