Enhancing spatial and textual analysis with EUPEG: an extensible and unified platform for evaluating geoparsers

TL;DR

EUPEG is an open-source, web-based platform that streamlines the evaluation and comparison of geoparsers by hosting datasets, tools, and metrics, facilitating more efficient spatial analysis from unstructured texts.

Contribution

The paper introduces EUPEG, a unified platform that simplifies benchmarking and comparison of geoparsers, addressing the challenge of inconsistent testing datasets and metrics.

Findings

Hosts multiple open corpora and geoparsers

Enables direct comparison of geoparsers

Reduces effort in preparing datasets for evaluation

Abstract

A rich amount of geographic information exists in unstructured texts, such as Web pages, social media posts, housing advertisements, and historical archives. Geoparsers are useful tools that extract structured geographic information from unstructured texts, thereby enabling spatial analysis on textual data. While a number of geoparsers were developed, they were tested on different datasets using different metrics. Consequently, it is difficult to compare existing geoparsers or to compare a new geoparser with existing ones. In recent years, researchers created open and annotated corpora for testing geoparsers. While these corpora are extremely valuable, much effort is still needed for a researcher to prepare these datasets and deploy geoparsers for comparative experiments. This paper presents EUPEG: an Extensible and Unified Platform for Evaluating Geoparsers. EUPEG is an open source and Web based benchmarking platform which hosts a majority of open corpora, geoparsers, and performance metrics reported in the literature. It enables direct comparison of the hosted geoparsers, and a new geoparser can be connected to EUPEG and compared with other geoparsers. The main objective of EUPEG is to reduce the time and effort that researchers have to spend in preparing datasets and baselines, thereby increasing the efficiency and effectiveness of comparative experiments.