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

A 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%.

Keywords

geospatial prepositions, biological specimen collections, georeferencing, natural language processing, locative expressions, locality descriptions, geoparsing, geocoding, geographic information retrieval, regression, machine learning

Citation

Liao 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.

URI

https://mro.massey.ac.nz/handle/10179/73027

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