A SVM and Co-seMLP Integrated Method for Document-Based Question Answering

Xiaoan Liu, Tao Peng
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Abstract

In this paper, we describe our features and models for Chinese Open-Domain Question Answering DBQA shared task in NLPCC-ICCPOL 2017. After the analysis of task and dataset, 8 features were extracted, and then 4 models were trained. Finally, our model achieves a result, in which MRR score is 0.494292 and MAP score is 0.491736.
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基于SVM和Co-seMLP的文档问答方法
在本文中,我们描述了我们在nlpcc - iccppol 2017中中文开放域问答DBQA共享任务的特征和模型。通过对任务和数据集的分析,提取出8个特征,然后训练出4个模型。最后,我们的模型得到了一个结果,其中MRR得分为0.494292,MAP得分为0.491736。
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