Geographical data can be obtained by converting place names from free-format text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.

中文翻译：

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地理解析中缺少什么？

可以通过将地名从自由格式的文本转换为地理坐标来获得地理数据。在文本报告中对事件进行地理位置定位的能力代表了许多现实世界应用程序中有价值的信息源，例如紧急响应，实时社交媒体地理事件分析，了解自动响应系统中的位置指示等。但是，正如我们在分析中所显示的，由于领域语言的多样性，地名的歧义性，转喻的语言以及上下文的有限利用，地理解析仍然被广泛认为是一项挑战。迄今为止的结果虽然很有希望，但仍在实验室数据中，与更广泛的NLP不同，通常不会进行交叉比较。在这项研究中，我们评估和分析了许多语料库上许多领先的地理解析器的性能，并详细说明了挑战。