What’s missing in geographical parsing?
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 geograp...
| Publicado en: | Language Resources & Evaluation Vol. 52; no. 2; pp. 603 - 624 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Jun2018
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=129593477&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 129593477 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2018 vid: 52 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 129593477 10.1007/s10579-017-9385-8 ppf: 603 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P size: 866KB tig: atl: What’s missing in geographical parsing? aug: au: Gritta, Milan Pilehvar, Mohammad Taher Limsopatham, Nut Collier, Nigel affil: Language Technology Lab (LTL), Department of Theoretical and Applied Linguistics (DTAL), University of Cambridge, 9 West Road, CB3 9DP, Cambridge, UK su: Parsing (Computer grammar) Geographic information systems Information resources management Open source software Geodatabases Semantics sug: subj: Parsing (Computer grammar) Geographic information systems Information resources management Open source software Geodatabases Semantics keyword: Geocoding Geoparsing Geotagging NED NEL NER NLP ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2018. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2018 holdings: @attributes: islocal: N |
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