Type-Aware Question Answering over Knowledge Base with Attention-Based   Tree-Structured Neural Networks

doi:10.1007/s11390-017-1761-8

CORC > 北京大学 > 信息科学技术学院

	Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks
	Yin, Jun ; Zhao, Wayne Xin ; Li, Xiao-Ming
刊名	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
	2017
关键词	question answering deep neural network knowledge base
DOI	10.1007/s11390-017-1761-8
英文摘要	Question answering (QA) over knowledge base (KB) aims to provide a structured answer from a knowledge base to a natural language question. In this task, a key step is how to represent and understand the natural language query. In this paper, we propose to use tree-structured neural networks constructed based on the constituency tree to model natural language queries. We identify an interesting observation in the constituency tree: different constituents have their own semantic characteristics and might be suitable to solve different subtasks in a QA system. Based on this point, we incorporate the type information as an auxiliary supervision signal to improve the QA performance. We call our approach type-aware QA. We jointly characterize both the answer and its answer type in a unified neural network model with the attention mechanism. Instead of simply using the root representation, we represent the query by combining the representations of different constituents using task-specific attention weights. Extensive experiments on public datasets have demonstrated the effectiveness of our proposed model. More specially, the learned attention weights are quite useful in understanding the query. The produced representations for intermediate nodes can be used for analyzing the effectiveness of components in a QA system.; National Basic Research 973 Program of China [2014CB340405]; National Natural Science Foundation of China [U1536201]; Beijing Natural Science Foundation [4162032]; Guangdong Province Key Laboratory of Big Data Analysis and Processing [2017001]; SCI(E); 中国科学引文数据库(CSCD); ARTICLE; 4; 805-813; 32
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/472411]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yin, Jun,Zhao, Wayne Xin,Li, Xiao-Ming. Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2017.
APA	Yin, Jun,Zhao, Wayne Xin,&Li, Xiao-Ming.(2017).Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY.
MLA	Yin, Jun,et al."Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2017).