CASIA OpenIR  > 模式识别国家重点实验室  > 自然语言处理
Liu Kang; Zhang Yuanzhe; Ji Guolaing; Lai Siwei; Zhao Jun
Other AbstractQuestion 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.
Document Type期刊论文
Corresponding AuthorLiu Kang
Recommended Citation
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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