SpatialML: annotation scheme, resources, and evaluation.
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, and characterizes relationships among places in terms of a region calculus. A freely available annotat...
| Publicado en: | Language Resources & Evaluation Vol. 44; no. 3; pp. 263 - 281 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Sep2010
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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=52370329&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 52370329 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2010 vid: 44 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 52370329 10.1007/s10579-010-9121-0 ppf: 263 ppct: 18 formats: fmt: @attributes: type: P size: 498KB tig: atl: SpatialML: annotation scheme, resources, and evaluation. aug: au: Mani, Inderjeet Doran, Christy Harris, Dave Hitzeman, Janet Quimby, Rob Richer, Justin Wellner, Ben Mardis, Scott Clancy, Seamus affil: The MITRE Corporation, 202 Burlington Road Bedford 01730 USA su: Language & languages Ethnology Annotations Corpora Linguistic analysis sug: subj: Language & languages Ethnology Annotations Corpora Linguistic analysis keyword: Adaptation Annotation Evaluation Geography Guidelines Information extraction Spatial language ab: 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, and characterizes relationships among places in terms of a region calculus. A freely available annotation editor has been developed for SpatialML, along with several annotated corpora. Inter-annotator agreement on SpatialML extents is 91.3 F-measure on a corpus of SpatialML-annotated ACE documents released by the Linguistic Data Consortium. Disambiguation agreement on geo-coordinates on ACE is 87.93 F-measure. An automatic tagger for SpatialML extents scores 86.9 F on ACE, while a disambiguator scores 93.0 F on it. Results are also presented for two other corpora. In adapting the extent tagger to new domains, merging the training data from the ACE corpus with annotated data in the new domain provides the best performance. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2010. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2010 holdings: @attributes: islocal: N |
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