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	Question Answering via Phrasal Semantic Parsing
	Xu, Kun ; Feng, Yansong ; Huang, Songfang ; Zhao, Dongyan
	2015
英文摘要	Understanding natural language questions and converting them into structured queries have been considered as a crucial way to help users access large scale structured knowledge bases. However, the task usually involves two main challenges: recognizing users' query intention and mapping the involved semantic items against a given knowledge base (KB). In this paper, we propose an efficient pipeline framework to model a user's query intention as a phrase level dependency DAG which is then instantiated regarding a specific KB to construct the final structured query. Our model benefits from the efficiency of linear structured prediction models and the separation of KB-independent and KB-related modelings. We evaluate our model on two datasets, and the experimental results showed that our method outperforms the state-of-the-art methods on the Free917 dataset, and, with limited training data from Free917, our model can smoothly adapt to new challenging dataset, WebQuestion, without extra training efforts while maintaining promising performances.; EI; CPCI-S(ISTP); xukun@pku.edu.cn; fengyansong@pku.edu.cn; huangsf@cn.ibm.com; zhaodongyan@pku.edu.cn; 414-426; 9283
语种	英语
出处	EXPERIMENTAL IR MEETS MULTILINGUALITY, MULTIMODALITY, AND INTERACTION
DOI标识	10.1007/978-3-319-24027-5_43
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/423552]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xu, Kun,Feng, Yansong,Huang, Songfang,et al. Question Answering via Phrasal Semantic Parsing. 2015-01-01.

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