Syntax-driven Data Augmentation for Named Entity Recognition

• URL: http://arxiv.org/abs/2208.06957v1
• Date: Mon, 15 Aug 2022 01:24:55 GMT
• Title: Syntax-driven Data Augmentation for Named Entity Recognition
• Authors: Arie Pratama Sutiono, Gus Hahn-Powell
• Abstract summary: In low resource settings, data augmentation strategies are commonly leveraged to improve performance. We compare simple masked language model replacement and an augmentation method using constituency tree mutations to improve named entity recognition.
• Score: 3.0603554929274908
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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