Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set

Hassan S. Shavarani, Satoshi Sekine


Abstract
Wikipedia is a great source of general world knowledge which can guide NLP models better understand their motivation to make predictions. Structuring Wikipedia is the initial step towards this goal which can facilitate fine-grain classification of articles. In this work, we introduce the Shinra 5-Language Categorization Dataset (SHINRA-5LDS), a large multi-lingual and multi-labeled set of annotated Wikipedia articles in Japanese, English, French, German, and Farsi using Extended Named Entity (ENE) tag set. We evaluate the dataset using the best models provided for ENE label set classification and show that the currently available classification models struggle with large datasets using fine-grained tag sets.
Anthology ID:
2020.lrec-1.150
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1197–1201
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.150
DOI:
Bibkey:
Cite (ACL):
Hassan S. Shavarani and Satoshi Sekine. 2020. Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1197–1201, Marseille, France. European Language Resources Association.
Cite (Informal):
Multi-class Multilingual Classification of Wikipedia Articles Using Extended Named Entity Tag Set (Shavarani & Sekine, LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.150.pdf