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PADI-Location-AR-EN: A normalized Arabic-English spatial entity dataset for epidemiological surveillance.

Fatima Ezzahra El Houbri1,2,3, Najlae Idrissi1, Mathieu Roche2,3

  • 1Intelligence, Data and Computing (IDC) Team, Faculty of Sciences and Techniques, Sultan Moulay Slimane University, Beni Mellal 23000, Morocco.

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This study introduces PADI-Location-AR-EN, a new dataset for Arabic spatial entity extraction in epidemiological monitoring. It aids in evaluating machine translation and named entity recognition for Arabic texts.

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Area of Science:

  • Natural Language Processing
  • Geospatial Information Systems
  • Public Health Informatics

Background:

  • Extracting event locations from Arabic texts is challenging for epidemiological monitoring.
  • Existing systems like PADI-web depend on English translations, but Arabic spatial entity data is scarce, impacting reliability.

Purpose of the Study:

  • To present PADI-Location-AR-EN, a novel dataset for Arabic spatial entity extraction.
  • To facilitate the evaluation of machine translation and named entity recognition (NER) models for Arabic epidemiological texts.

Main Methods:

  • Manually extracted 328 spatial entities from 96 Arabic news articles.
  • Translated entities to English, normalized them using GeoNames, and classified their types and spatial categories.
  • The dataset was used to assess three machine translation systems and NER model performance.

Main Results:

  • The PADI-Location-AR-EN dataset provides a benchmark for evaluating NLP tools on Arabic epidemiological data.
  • Demonstrates the utility of the dataset for assessing translation quality and NER accuracy.
  • Highlights the challenges and potential improvements in processing multilingual health information.

Conclusions:

  • PADI-Location-AR-EN is a valuable resource for advancing NLP in Arabic epidemiological surveillance.
  • Improved machine translation and NER are crucial for reliable spatial entity extraction from multilingual sources.
  • This dataset supports research in cross-lingual information retrieval for public health.