Streamlining event extraction with a simplified annotation framework

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ATJQ66J84" target="_blank" >RIV/00216208:11320/25:TJQ66J84 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192957688&doi=10.3389%2ffrai.2024.1361483&partnerID=40&md5=99dbcba6e126c86c6b93390eef61f2b6" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85192957688&doi=10.3389%2ffrai.2024.1361483&partnerID=40&md5=99dbcba6e126c86c6b93390eef61f2b6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/frai.2024.1361483" target="_blank" >10.3389/frai.2024.1361483</a>

Result language
angličtina
Original language name
Streamlining event extraction with a simplified annotation framework
Original language description
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. Copyright © 2024 Saetia, Thonglong, Amornchaiteera, Chalothorn, Taerungruang and Buabthong.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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