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| 基于上下文的深度语义句子检索模型 | |
| 引用本文： | 范意兴,郭嘉丰,兰艳艳,徐君,程学旗.基于上下文的深度语义句子检索模型[J].中文信息学报,2017,31(5):156-162. |
| 作者姓名： | 范意兴 郭嘉丰 兰艳艳 徐君 程学旗 |
| 作者单位： | 1.中国科学院网络数据科学与技术重点实验室 中国科学院 计算技术研究所,北京 100190; 2.中国科学院大学,北京 100190 |
| 基金项目： | 国家重点基础研究发展计划(“973”计划)(2014CB340401,2013329606);科技部重点研发计划(2016QY02D0405);国家自然科学基金(61232010,61472401,61425016,61203298);中国科学院青年创新促进会优秀会员项目(20144310,2016102) |
| 摘 要： | 传统的信息检索的研究多集中在文档级的检索场景中,然而,句子级的检索在如移动应用以及信息需求更加明确的检索场景下具有非常重要的意义。在句子级的检索场景下,我们认为句子的上下文能够提供更加丰富的语义信息来支撑句子与查询的匹配,基于此,该文提出了一个基于句子上下文的深度语义句子检索模型(context-aware deep sentence matching model, CDSMM)。具体的,我们使用双向循环神经网络来建模句子内部以及句子上下文的语义信息,基于句子和查询的语义信息得到它们的匹配程度,在WebAP句子检索数据集上的实验表明,我们的模型性能显著地优于其他的方法,并取得了目前最好的效果。 |
| 关 键 词： | 信息检索 文本匹配 循环神经网络 |
A Context-aware Deep Sentence Matching Model |
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| FAN Yixing,GUO Jiafeng,LAN Yanyan,XU Jun,CHENG Xueqi.A Context-aware Deep Sentence Matching Model[J].Journal of Chinese Information Processing,2017,31(5):156-162. | |
| Authors: | FAN Yixing GUO Jiafeng LAN Yanyan XU Jun CHENG Xueqi |
| Affiliation: | 1.CAS Key Lab of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; 2.University of Chinese Academy of Sciences, Beijing 100190, China |
| Abstract: | Traditional researches on information retrieval are focuse on document-level retrieval, neglecting, sentence-level information retrieval which is of great importance in such applications, as searching in mobile phone Assuming that the context sentence could provide richer evidence for matching. this paper proposes a context-aware deep sentence matching model(CDSMM). Specifically, the model employs bi-directional LSTM to capture the interior and exterior information of the sentence; Then, a matching matrix is constructed based on the sentence representation and query representation; Finally, we get the matching score after a feed forward neural network. Experiment results on the WebAP dataset show that out model can significantly out-perform the state-of-the-art models. |
| Keywords: | information retrieval text matching RNN |
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