Syntax-driven Data Augmentation for Named Entity Recognition

Arie Sutiono, Gus Hahn-Powell


Abstract
In low resource settings, data augmentation strategies are commonly leveraged to improve performance. Numerous approaches have attempted document-level augmentation (e.g., text classification), but few studies have explored token-level augmentation. Performed naively, data augmentation can produce semantically incongruent and ungrammatical examples. In this work, we compare simple masked language model replacement and an augmentation method using constituency tree mutations to improve the performance of named entity recognition in low-resource settings with the aim of preserving linguistic cohesion of the augmented sentences.
Anthology ID:
2022.pandl-1.7
Volume:
Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Laura Chiticariu, Yoav Goldberg, Gus Hahn-Powell, Clayton T. Morrison, Aakanksha Naik, Rebecca Sharp, Mihai Surdeanu, Marco Valenzuela-Escárcega, Enrique Noriega-Atala
Venue:
PANDL
SIG:
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
56–60
Language:
URL:
https://aclanthology.org/2022.pandl-1.7
DOI:
Bibkey:
Cite (ACL):
Arie Sutiono and Gus Hahn-Powell. 2022. Syntax-driven Data Augmentation for Named Entity Recognition. In Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning, pages 56–60, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
Syntax-driven Data Augmentation for Named Entity Recognition (Sutiono & Hahn-Powell, PANDL 2022)
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PDF:
https://aclanthology.org/2022.pandl-1.7.pdf
Code
 ariepratama/syntax-driven-da