Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
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DOI：
10.3115/v1/p15-1128
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发表时间：
2015-07
期刊：
影响因子：
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通讯作者：
Wen-tau Yih;Ming-Wei Chang;Xiaodong He;Jianfeng Gao
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文献类型：
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作者：
Wen-tau Yih;Ming-Wei Chang;Xiaodong He;Jianfeng Gao
我们提出了一个新的语义分析框架的问题回答使用的知识库。我们定义了一个查询图，类似于知识库的子图，可以直接映射到一个逻辑形式。语义分析被简化为查询图的生成，制定为一个分阶段的搜索问题。与传统方法不同，我们的方法利用知识库在早期阶段修剪搜索空间，从而简化了语义匹配问题。通过应用先进的实体链接系统和匹配问题和谓词序列的深度卷积神经网络模型，我们的系统大大优于以前的方法，并在WEBQUESTIONS数据集上实现了52.5%的F1指标。
We propose a novel semantic parsing framework for question answering using a knowledge base. We define a query graph that resembles subgraphs of the knowledge base and can be directly mapped to a logical form. Semantic parsing is reduced to query graph generation, formulated as a staged search problem. Unlike traditional approaches, our method leverages the knowledge base in an early stage to prune the search space and thus simplifies the semantic matching problem. By applying an advanced entity linking system and a deep convolutional neural network model that matches questions and predicate sequences, our system outperforms previous methods substantially, and achieves an F1 measure of 52.5% on the WEBQUESTIONS dataset.