Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract)
Keywords:Named Entity Recognition, Adversarial Adaptation, Transformers, Limited-resource Languages, Unlabelled Corpora
AbstractNamed Entity Recognition (NER) involves the identification and classification of named entities in unstructured text into predefined classes. NER in languages with limited resources, like French, is still an open problem due to the lack of large, robust, labelled datasets. In this paper, we propose a transformer-based NER approach for French using adversarial adaptation to similar domain or general corpora for improved feature extraction and better generalization. We evaluate our approach on three labelled datasets and show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of transformer models, source datasets and target corpora.
How to Cite
Choudhry, A., Gupta, P., Khatri, I., Gupta, A., Nicol, M., Meurs, M.-J., & Vishwakarma, D. K. (2023). Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16196-16197. https://doi.org/10.1609/aaai.v37i13.26958
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