SpatialML: Annotation Scheme, Corpora, and Tools
SpatialML: Annotation Scheme, Corpora, and Tools
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SpatialML：注释方案、语料库和工具
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发表时间：
2008
期刊：
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通讯作者：
Ben Wellner
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作者：
I. Mani;J. Hitzeman;Justin Richer;Dave Harris;R. Quimby;Ben Wellner
空间ML是一种注释方案，用于标记对自然语言的参考。它涵盖了命名和名义引用到地点，并在可能的地方将它们扎根，包括相对位置和绝对位置，并在区域微积分方面表征了位置之间的关系。为空间ML开发了一份免费的注释编辑器，以及语言数据联盟发布的注释文档的语料库。关于空间ML的通道间一致性是该语料库的量级的77.0 f量。空间ML的自动标记器得分为78.5 f-measure。 DiSamugutor得分为93.0 F量和93.4预测精度。在将标记器的范围调整为新域时，将上述语料库中的培训数据与新域中的注释数据合并为最佳性能。
SpatialML is an annotation scheme for marking up references to places in natural language. It covers both named and nominal references to places, grounding them where possible with geo-coordinates, including both relative and absolute locations, and characterizes relationships among places in terms of a region calculus. A freely available annotation editor has been developed for SpatialML, along with a corpus of annotated documents released by the Linguistic Data Consortium. Inter-annotator agreement on SpatialML is 77.0 F-measure for extents on that corpus. An automatic tagger for SpatialML extents scores 78.5 F-measure. A disambiguator scores 93.0 F-measure and 93.4 Predictive Accuracy. In adapting the extent tagger to new domains, merging the training data from the above corpus with annotated data in the new domain provides the best performance.