Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections

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dc.citation.issueArticle No: 4
dc.citation.volume240
dc.contributor.authorLiao R
dc.contributor.authorDas PP
dc.contributor.authorJones CB
dc.contributor.authorAflaki N
dc.contributor.authorStock K
dc.contributor.editorIshikawa T
dc.contributor.editorFabrikant SI
dc.contributor.editorWinter S
dc.coverage.spatialKobe, Japan
dc.date.accessioned2025-06-11T01:28:04Z
dc.date.available2025-06-11T01:28:04Z
dc.date.finish-date2022-09-09
dc.date.issued2022-08-22
dc.date.start-date2022-09-05
dc.description.abstractA considerable proportion of records that describe biological specimens (flora, soil, invertebrates), and especially those that were collected decades ago, are not attached to corresponding geographical coordinates, but rather have their location described only through textual descriptions (e.g. North Canterbury, Selwyn River near bridge on Springston-Leeston Rd). Without geographical coordinates, millions of records stored in museum collections around the world cannot be mapped. We present a method for predicting the distance and direction associated with human language location descriptions which focuses on the interpretation of geospatial prepositions and the way in which they modify the location represented by an associated reference place name (e.g. near the Manawatu River). We study eight distance-oriented prepositions and eight direction-oriented prepositions and use machine learning regression to predict distance or direction, relative to the reference place name, from a collection of training data. The results show that, compared with a simple baseline, our model improved distance predictions by up to 60% and direction predictions by up to 31%.
dc.description.confidentialfalse
dc.description.place-of-publicationGermany
dc.identifier.citationLiao R, Das PP, Jones CB, Aflaki N, Stock K. (2022). Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections. Ishikawa T, Fabrikant SI, Winter S. Leibniz International Proceedings in Informatics (LIPIcs). Germany. Schloss Dagstuhl – Leibniz-Zentrum für Informatik.
dc.identifier.doi10.4230/LIPIcs.COSIT.2022.4
dc.identifier.elements-typec-conference-paper-in-proceedings
dc.identifier.issn1868-8969
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/73027
dc.publisherSchloss Dagstuhl – Leibniz-Zentrum für Informatik
dc.publisher.urihttp://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.4
dc.rights(c) The author/sen
dc.rights.licenseCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.source.journalLeibniz International Proceedings in Informatics (LIPIcs)
dc.source.name-of-conference15th International Conference on Spatial Information Theory (COSIT 2022)
dc.subjectgeospatial prepositions
dc.subjectbiological specimen collections
dc.subjectgeoreferencing
dc.subjectnatural language processing
dc.subjectlocative expressions
dc.subjectlocality descriptions
dc.subjectgeoparsing
dc.subjectgeocoding
dc.subjectgeographic information retrieval
dc.subjectregression
dc.subjectmachine learning
dc.titlePredicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections
dc.typeconference
pubs.elements-id457011
pubs.organisational-groupOther
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