Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on t...Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on the question-answer joint learning(ACLSTM).The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer(Q&A)pairs.Convolutional Neural Network(CNN)and Long Short-term Memory Network(LSTM)are used to extract semantic features of Q&A pairs and calculate their matching degree.Besides,answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer.Compared with other quality prediction models,the ACLSTM model can effectively improve the prediction effect of answer quality.In particular,the mediumquality answer prediction,and its prediction effect is improved after adding effective extended features.Experiments prove that after the ACLSTM model learning,the Q&A pairs can better measure the semantic match between each other,fully reflecting the model’s superior performance in the semantic information processing of the answer text.展开更多
为研究面向大规模网络数据的社会化问答系统(Social Question and Answer System,SocialQA).分别描述了问答系统的各个组成技术:1)问句预处理:问句分析和问句扩展.2)问句匹配.本文在1500万个网络问答数据集上,进行了问句匹配的实验.实...为研究面向大规模网络数据的社会化问答系统(Social Question and Answer System,SocialQA).分别描述了问答系统的各个组成技术:1)问句预处理:问句分析和问句扩展.2)问句匹配.本文在1500万个网络问答数据集上,进行了问句匹配的实验.实验表明:在封闭测试中,问句匹配的准确率,达到了90%以上,在开放测试中,问句匹配的准确率达到了70%以上,很好地满足了系统的精度和实时性的要求.展开更多
基金the Zhejiang Provincial Natural Science Foundation of China under Grant No.LGF18F020011.
文摘Given the limitations of the community question answering(CQA)answer quality prediction method in measuring the semantic information of the answer text,this paper proposes an answer quality prediction model based on the question-answer joint learning(ACLSTM).The attention mechanism is used to obtain the dependency relationship between the Question-and-Answer(Q&A)pairs.Convolutional Neural Network(CNN)and Long Short-term Memory Network(LSTM)are used to extract semantic features of Q&A pairs and calculate their matching degree.Besides,answer semantic representation is combined with other effective extended features as the input representation of the fully connected layer.Compared with other quality prediction models,the ACLSTM model can effectively improve the prediction effect of answer quality.In particular,the mediumquality answer prediction,and its prediction effect is improved after adding effective extended features.Experiments prove that after the ACLSTM model learning,the Q&A pairs can better measure the semantic match between each other,fully reflecting the model’s superior performance in the semantic information processing of the answer text.
文摘为研究面向大规模网络数据的社会化问答系统(Social Question and Answer System,SocialQA).分别描述了问答系统的各个组成技术:1)问句预处理:问句分析和问句扩展.2)问句匹配.本文在1500万个网络问答数据集上,进行了问句匹配的实验.实验表明:在封闭测试中,问句匹配的准确率,达到了90%以上,在开放测试中,问句匹配的准确率达到了70%以上,很好地满足了系统的精度和实时性的要求.
