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	What is the longest river in the USA? Semantic parsing for aggregation questions
	Xu, Kun ; Zhang, Sheng ; Feng, Yansong ; Huang, Songfang ; Zhao, Dongyan
	2015
英文摘要	Answering natural language questions against structured knowledge bases (KB) has been attracting increasing attention in both IR and NLP communities. The task involves two main challenges: recognizing the questions' meanings, which are then grounded to a given KB. Targeting simple factoid questions, many existing open domain semantic parsers jointly solve these two subtasks, but are usually expensive in complexity and resources. In this paper, we propose a simple pipeline framework to efficiently answer more complicated questions, especially those implying aggregation operations, e.g., argmax, argmin. We first develop a transitionbased parsing model to recognize the KB-independent meaning representation of the user's intention inherent in the question. Secondly, we apply a probabilistic model to map the meaning representation, including those aggregation functions, to a structured query. The experimental results showe that our method can better understand aggregation questions, outperforming the state-of-the-art methods on the Free917 dataset while still maintaining promising performance on a more challenging dataset, WebQuestions, without extra training. ? Copyright 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; EI; 4222-4223; 6
语种	英语
出处	29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436822]
专题	计算机科学技术研究所
推荐引用方式 GB/T 7714	Xu, Kun,Zhang, Sheng,Feng, Yansong,et al. What is the longest river in the USA? Semantic parsing for aggregation questions. 2015-01-01.

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