神经因子式地理空间问答

IF 1.8 Q2 GEOGRAPHY
Haonan Li, E. Hamzei, I. Majić, Hua Hua, Jochen Renz, M. Tomko, M. Vasardani, S. Winter, Timothy Baldwin
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引用次数: 2

摘要

当涉及地理空间信息时,现有的问答系统很难回答事实问题。这是因为大多数系统无法从自然语言问题中准确检测地理空间语义元素,也无法捕捉这些元素之间的语义关系。在本文中,我们提出了一种地理空间语义编码模式和一种语义图表示,它捕捉了地理空间问题中的语义关系和依赖关系。我们证明了我们提出的图表示方法有助于从自然语言转换为查询语言中的正式可执行表达式。为了减少人们在问题中提供解释性信息的需求,并使翻译完全自动化,我们将问题的语义编码视为一个顺序标记任务,将查询的图形生成视为语义依赖解析任务。我们应用神经网络方法将地理空间问题自动编码为空间语义图表示。与当前基于模板的方法相比,我们的方法适用于更广泛的问题,包括那些具有复杂语法和语义的问题。我们提出的方法在GeoData201上获得了比现有方法更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural factoid geospatial question answering
Existing question answering systems struggle to answer factoid questions when geospatial information is involved. This is because most systems cannot accurately detect the geospatial semantic elements from the natural language questions, or capture the semantic relationships between those elements. In this paper, we propose a geospatial semantic encoding schema and a semantic graph representation which captures the semantic relations and dependencies in geospatial questions. We demonstrate that our proposed graph representation approach aids in the translation from natural language to a formal, executable expression in a query language. To decrease the need for people to provide explanatory information as part of their question and make the translation fully automatic, we treat the semantic encoding of the question as a sequential tagging task, and the graph generation of the query as a semantic dependency parsing task. We apply neural network approaches to automatically encode the geospatial questions into spatial semantic graph representations. Compared with current template-based approaches, our method generalises to a broader range of questions, including those with complex syntax and semantics. Our proposed approach achieves better results on GeoData201 than existing methods.
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来源期刊
CiteScore
5.10
自引率
0.00%
发文量
5
审稿时长
9 weeks
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