CODECHECK Certificate 2022-013

Paper details

Title: GeoXTag: Relative Spatial Information Extraction and Tagging of Unstructured Text

Authors: Mehtab Alam Syed, Elena Arsevska, Mathieu Roche, Maguelonne Teisseire

Abstract: Obtained from CrossRef

Abstract. Spatial information has gained more attention in natural language processing tasks in different interdisciplinary domains. Moreover, the spatial information is available in two forms: Absolute Spatial Information (ASI) e.g., Paris, London, and Germany and Relative Spatial Information (RSI) e.g., south of Paris, north Madrid and 80 km from Rome. Therefore, it is challenging to extract RSI from textual data and compute its geotagging. This paper presents two strategies and the associated prototypes to address the following tasks: 1) extraction of relative spatial information from textual data and 2) geotagging of this relative spatial information. Experiments show promising results for RSI extraction and tagging.

Codecheck details

Certificate identifier: 2022-013

Codechecker name: Jakub Krukar

Time of codecheck: 2022-07-09 12:00:00

Repository: https://osf.io/3G9S8

Codecheck report: https://doi.org/10.17605/osf.io/3g9s8

Summary:

The main contribution of the paper are two web applications on the streamlit.io platform. The paper includes the DASA section and links to live online instances of the applications, as well as to GitHub repositories with the code and data. The repositories are well documented. I was able to clone the repositories and run the code, reproducing examples demonstrated on figures in the paper. One of the reproduced figures differed from the one presented in the paper. The tables demonstrating results of the evaluation of the app were partially reproducible but returned slightly different values. The paper has been partially reproduced.

CODECHECK Certificate 2022-013

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