Dynamic Ensembles in Named Entity Recognition for Historical Arabic Texts

Muhammad Majadly, Tomer Sagi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The use of Named Entity Recognition (NER) over archaic Arabic texts is steadily increasing. However, most tools have been either developed for modern English or trained over English language documents and are limited over historical Arabic text. Even Arabic NER tools are often trained on modern web-sourced text, making their fit for a historical task questionable. To mitigate historic Arabic NER resource scarcity, we propose a dynamic ensemble model utilizing several learners. The dynamic aspect is achieved by utilizing predictors and features over NER algorithm results that identify which have performed better on a specific task in real-time. We evaluate our approach against state-of-the-art Arabic NER and static ensemble methods over a novel historical Arabic NER task we have created. Our results show that our approach improves upon the state-of-the-art and reaches a 0.8 F-score on this challenging task.

Original languageEnglish
Title of host publicationWANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop
EditorsNizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
PublisherAssociation for Computational Linguistics (ACL)
Pages115-125
Number of pages11
ISBN (Electronic)9781954085091
StatePublished - 2021
Event6th Arabic Natural Language Processing Workshop, WANLP 2021 - Virtual, Kyiv, Ukraine
Duration: 19 Apr 2021 → …

Publication series

NameWANLP 2021 - 6th Arabic Natural Language Processing Workshop, Proceedings of the Workshop

Conference

Conference6th Arabic Natural Language Processing Workshop, WANLP 2021
Country/TerritoryUkraine
CityVirtual, Kyiv
Period19/04/21 → …

Bibliographical note

Publisher Copyright:
© WANLP 2021 - 6th Arabic Natural Language Processing Workshop

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Software
  • Linguistics and Language

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