Use of the Edinburgh geoparser for georeferencing digitized historical collections

Grover, Claire; Tobin, Richard; Byrne, Kate; Woollard, Matthew; Reid, James; Dunn, Stuart; Ball, Julian

doi:10.1098/rsta.2010.0149

Cited by 90 publications

(91 citation statements)

References 9 publications

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“…We are by no means the first to experiment with such approaches (see, for example, [5]) but we are not aware of similar methods being applied to ancient geography or massive corpora. The results of this processing will be RDF annotations for each text that provide an ordered directory of places.…”

Section: Methodsmentioning

confidence: 99%

GAP: A NeoGeo Approach to Classical Resources

et al. 2012

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Google Ancient Places (GAP) is a Google Digital Humanities Award recipient that will mine the Google Books corpus for classical material that has a strong geographic and historical basis. GAP will allow scholars, students, and enthusiasts world-wide to query the Google Books corpus to ask for books related to a geographic location or to ask for the locations referred to in a classical text. We will overcome the traditional difficulty of identifying place names by using a combination of URI-based gazetteers and an identification algorithm that associates the linear clustering of places within narrative texts with the geographic clustering of locations in the real world.

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Section: Methodsmentioning

confidence: 99%

GAP: A NeoGeo Approach to Classical Resources

et al. 2012

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“…), which is a subset of toponyms. This is the standard approach (DeLozier et al 2015;Grover et al 2010) in most geoparsing. However, other work (Gelernter and Balaji 2013) evaluated additional toponyms such as landmarks, buildings, street names, points of interest and outdoor areas (parks, etc).…”

Section: Introductionmentioning

confidence: 99%

What’s missing in geographical parsing?

Gritta

Pilehvar

Limsopatham

et al. 2017

Lang Resources & Evaluation

122

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Geographical data can be obtained by converting place names from freeformat 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.

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“…for "Co. Down", "Down" is an alternate name). Space considerations preclude a more detailed discussion but further information about our geotagger can be found in [6] and [7].…”

Section: System Overviewmentioning

confidence: 99%

“…The development of our georeferencing system has most recently been supported by two JISC-funded projects, GeoDigRef 7 and Embedding GeoCrossWalk 8 . The projects aimed to enrich the metadata of digitised historical collections with georeferences and other information automatically computed using information extraction and georesolution technologies.…”

Section: Evaluation Datamentioning

confidence: 99%

Evaluation of georeferencing

Tobin

Grover

Byrne

et al. 2010

Proceedings of the 6th Workshop on Geographic Information Retrieval

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In this paper we describe a georeferencing system which first uses Information Extraction techniques to identify place names in textual documents and which then resolves the place names against a choice of gazetteers. We have used the system to georeference three digitised historical collections and have evaluated its performance against human annotated gold standard samples from the three collections. We have also evaluated its performance on the SpatialML corpus which is a geo-annotated corpus of newspaper text. The main focus of this paper is the evaluation of georesolution and we discuss evaluation methods and issues arising from the evaluation.

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Use of the Edinburgh geoparser for georeferencing digitized historical collections

Cited by 90 publications

References 9 publications

GAP: A NeoGeo Approach to Classical Resources

GAP: A NeoGeo Approach to Classical Resources

What’s missing in geographical parsing?

Evaluation of georeferencing

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