Transformer-Based Named Entity Recognition for French Using Adversarial Adaptation to Similar Domain Corpora (Student Abstract) Article Swipe

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Arjun Choudhry , Pankaj Gupta , Inder Khatri , Aaryan Gupta , Maxime Nicol , Marie‐Jean Meurs , Dinesh Kumar Vishwakarma ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1609/aaai.v37i13.26958

Named 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.

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Named-entity recognition Computer science Adversarial system Transformer Domain adaptation Artificial intelligence Natural language processing Adaptation (eye) Named entity Generalization Engineering Mathematics Mathematical analysis Physics Voltage Optics Systems engineering Task (project management) Classifier (UML) Electrical engineering

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Type: article
Language: en
Landing Page: https://doi.org/10.1609/aaai.v37i13.26958
PDF: https://ojs.aaai.org/index.php/AAAI/article/download/26958/26730
OA Status: diamond
Cited By: 2
References: 8
Related Works: 10
OpenAlex ID: https://openalex.org/W4382318276

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