CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
基于表示学习的知识库问答研究进展与展望
Liu Kang; Zhang Yuanzhe; Ji Guolaing; Lai Siwei; Zhao Jun
2016
发表期刊ACTA AUTOMATICA SINICA
期号42页码:807-818
其他摘要Question answering over knowledge base (KBQA) is an important direction for the research of question answering. Recently, with the drastic development of deep learning, researchers and developers have paid more attentions to KBQA from this angle. They regarded this problem as a task of semantic matching. The semantics of knowledge base and users' questions are learned through representation learning under the framework of deep learning. The entities and relations in knowledge base and the texts in questions could be represented as numerical vectors. Then, the answer could be figured out through similarity computation between the vectors of knowledge base and the vectors of the given question. From reported results, KBQA based on representation learning has obtained the best performance. This paper introduces the mainstream methods in this area. It further induces the typical approaches of representation learning on knowledge base and texts (questions), respectively. Finally, the current research challenges are discussed.
关键词表示学习
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20670
专题模式识别国家重点实验室_自然语言处理
通讯作者Liu Kang
推荐引用方式
GB/T 7714
Liu Kang,Zhang Yuanzhe,Ji Guolaing,等. 基于表示学习的知识库问答研究进展与展望[J]. ACTA AUTOMATICA SINICA,2016(42):807-818.
APA Liu Kang,Zhang Yuanzhe,Ji Guolaing,Lai Siwei,&Zhao Jun.(2016).基于表示学习的知识库问答研究进展与展望.ACTA AUTOMATICA SINICA(42),807-818.
MLA Liu Kang,et al."基于表示学习的知识库问答研究进展与展望".ACTA AUTOMATICA SINICA .42(2016):807-818.
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