Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2018 | Language Resources and Evaluation | Geocoding,Geoparsing,Geotagging,NED,NEL,NER,NLP |
DocType | Volume | Issue |
Journal | 52 | 2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Milan Gritta | 1 | 2 | 1.75 |
| Mohammad Taher Pilehvar | 2 | 376 | 25.70 |
| Nut Limsopatham | 3 | 172 | 14.86 |
| Nigel Collier | 4 | 1164 | 96.59 |