Detecting geospatial location descriptions in natural language text

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dc.citation.issue3
dc.citation.volume36
dc.contributor.authorStock K
dc.contributor.authorJones CB
dc.contributor.authorRussell S
dc.contributor.authorRadke M
dc.contributor.authorDas P
dc.contributor.authorAflaki N
dc.date.accessioned2023-11-22T20:45:14Z
dc.date.accessioned2024-07-25T06:50:42Z
dc.date.available2021-12-22
dc.date.available2023-11-22T20:45:14Z
dc.date.available2024-07-25T06:50:42Z
dc.date.issued2022
dc.description.abstractReferences to geographic locations are common in text data sources including social media and web pages. They take different forms from simple place names to relative expressions that describe location through a spatial relationship to a reference object (e.g. the house beside the Waikato River). Often complex, multi-word phrases are employed (e.g. the road and railway cross at right angles; the road in line with the canal) where spatial relationships are communicated with various parts of speech including prepositions, verbs, adverbs and adjectives. We address the problem of automatically detecting relative geospatial location descriptions, which we define as those that include spatial relation terms referencing geographic objects, and distinguishing them from non-geographical descriptions of location (e.g. the book on the table). We experiment with several methods for automated classification of text expressions, using features for machine learning that include bag of words that detect distinctive words, word embeddings that encode meanings of words and manually identified language patterns that characterise geospatial expressions. Using three data sets created for this study, we find that ensemble and meta-classifier approaches, that variously combine predictions from several other classifiers with data features, provide the best F-measure of 0.90 for detecting geospatial expressions.
dc.description.confidentialfalse
dc.format.pagination547-584
dc.identifier.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000734210300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationStock K, Jones CB, Russell S, Radke M, Das P, Aflaki N. (2022). Detecting geospatial location descriptions in natural language text. International Journal of Geographical Information Science. 36. 3. (pp. 547-584).
dc.identifier.doi10.1080/13658816.2021.1987441
dc.identifier.eissn1362-3087
dc.identifier.elements-typejournal-article
dc.identifier.issn1365-8816
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70997
dc.languageEnglish
dc.publisherTaylor and Francis Group
dc.publisher.urihttps://www.tandfonline.com/doi/full/10.1080/13658816.2021.1987441
dc.relation.isPartOfInternational Journal of Geographical Information Science
dc.rights(c) 2021 The Author/s
dc.rightsCC BY-NC-ND 4.0
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectGeospatial language
dc.subjectnatural language processing
dc.subjectspatial role labelling
dc.subjectgeospatial parsing
dc.subjectspatial relations
dc.subjectlocative expressions
dc.subjectgeoreferencing
dc.subjectgeographic information retrieval
dc.titleDetecting geospatial location descriptions in natural language text
dc.typeJournal article
pubs.elements-id450280
pubs.organisational-groupOther
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