Frontiers in Artificial Intelligence (Apr 2024)

Streamlining event extraction with a simplified annotation framework

  • Chanatip Saetia,
  • Areeya Thonglong,
  • Thanpitcha Amornchaiteera,
  • Tawunrat Chalothorn,
  • Supawat Taerungruang,
  • Pakpoom Buabthong

DOI
https://doi.org/10.3389/frai.2024.1361483
Journal volume & issue
Vol. 7

Abstract

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Event extraction, grounded in semantic relationships, can serve as a simplified relation extraction. In this study, we propose an efficient open-domain event annotation framework tailored for subsequent information extraction, with a specific focus on its applicability to low-resource languages. The proposed event annotation method, which is based on event semantic elements, demonstrates substantial time-efficiency gains over traditional Universal Dependencies (UD) tagging. We show how language-specific pretraining outperforms multilingual counterparts in entity and relation extraction tasks and emphasize the importance of task- and language-specific fine-tuning for optimal model performance. Furthermore, we demonstrate the improvement of model performance upon integrating UD information during pre-training, achieving the F1 score of 71.16 and 60.43% for entity and relation extraction respectively. In addition, we showcase the usage of our extracted event graph for improving node classification in a retail banking domain. This work provides valuable guidance on improving information extraction and outlines a methodology for developing training datasets, particularly for low-resource languages.

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