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A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap

机译:用于多语种自然语言查询OpenStreetMap的语料库和语义解析器

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We present a corpus of 2,380 natural language queries paired with machine readable formulae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We use the corpus to learn an accurate semantic parser that builds the basis of a natural language interface to OSM. Furthermore, we use response-based learning on parser feedback to adapt a statistical machine translation system for multilingual database access to OSM. Our framework allows to map fuzzy natural language expressions such as "nearby", "north of, or "in walking distance" to spatial polygons on an interactive map. Furthermore, it combines syntactic complexity and compositionality with a reasonable lexical variability of queries, making it an interesting new publicly available dataset for research on semantic parsing.
机译:我们提出了一种与机器可读公式配对的2,380个自然语言查询的语料库,可以针对OpenStreetMap(OSM)数据库的全球范围的地理数据执行。我们使用语料库来学习一个准确的语义解析器,该解析器构建自然语言界面的基础到OSM。此外,我们使用基于响应的学习对解析器反馈来调整统计机器翻译系统,以便对OSM进行多语言数据库访问。我们的框架允许将模糊的自然语言表达(如“附近)”,“北部”,“步行距离”,“在步行距离”中到互动地图上的空间多边形。此外,它结合了句法复杂性和组成性与合理的疑问的词汇变异性,制作它是一个有趣的新公共数据集,用于语义解析。

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