Adversarial Adaptation for French Named Entity Recognition Article Swipe
Arjun Choudhry
,
Inder Khatri
,
Pankaj Gupta
,
Aaryan Gupta
,
Maxime Nicol
,
Marie‐Jean Meurs
,
Dinesh Kumar Vishwakarma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.05220
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2301.05220
Named Entity Recognition (NER) is the task of identifying and classifying named entities in large-scale texts into predefined classes. NER in French and other relatively limited-resource languages cannot always benefit from approaches proposed for languages like English due to a dearth of large, robust datasets. In this paper, we present our work that aims to mitigate the effects of this dearth of large, labeled datasets. We propose a Transformer-based NER approach for French, using adversarial adaptation to similar domain or general corpora to improve feature extraction and enable better generalization. Our approach allows learning better features using large-scale unlabeled corpora from the same domain or mixed domains to introduce more variations during training and reduce overfitting. Experimental results on three labeled datasets show that our adaptation framework outperforms the corresponding non-adaptive models for various combinations of Transformer models, source datasets, and target corpora. We also show that adversarial adaptation to large-scale unlabeled corpora can help mitigate the performance dip incurred on using Transformer models pre-trained on smaller corpora.
Related Topics
Concepts
Overfitting
Computer science
Named-entity recognition
Domain adaptation
Transformer
Adversarial system
Artificial intelligence
Natural language processing
Machine learning
Adaptation (eye)
Task (project management)
Labeled data
Artificial neural network
Classifier (UML)
Physics
Optics
Economics
Voltage
Quantum mechanics
Management
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2301.05220
- https://arxiv.org/pdf/2301.05220
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4316135769
All OpenAlex metadata
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https://openalex.org/W4316135769Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2301.05220Digital Object Identifier
- Title
-
Adversarial Adaptation for French Named Entity RecognitionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-12Full publication date if available
- Authors
-
Arjun Choudhry, Inder Khatri, Pankaj Gupta, Aaryan Gupta, Maxime Nicol, Marie‐Jean Meurs, Dinesh Kumar VishwakarmaList of authors in order
- Landing page
-
https://arxiv.org/abs/2301.05220Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2301.05220Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2301.05220Direct OA link when available
- Concepts
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Overfitting, Computer science, Named-entity recognition, Domain adaptation, Transformer, Adversarial system, Artificial intelligence, Natural language processing, Machine learning, Adaptation (eye), Task (project management), Labeled data, Artificial neural network, Classifier (UML), Physics, Optics, Economics, Voltage, Quantum mechanics, ManagementTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.cannot | 27 |
| abstract_inverted_index.dearth | 40, 60 |
| abstract_inverted_index.domain | 78, 103 |
| abstract_inverted_index.during | 111 |
| abstract_inverted_index.enable | 87 |
| abstract_inverted_index.large, | 42, 62 |
| abstract_inverted_index.models | 131, 163 |
| abstract_inverted_index.paper, | 47 |
| abstract_inverted_index.reduce | 114 |
| abstract_inverted_index.robust | 43 |
| abstract_inverted_index.source | 138 |
| abstract_inverted_index.target | 141 |
| abstract_inverted_index.English | 36 |
| abstract_inverted_index.French, | 72 |
| abstract_inverted_index.benefit | 29 |
| abstract_inverted_index.corpora | 81, 99, 152 |
| abstract_inverted_index.domains | 106 |
| abstract_inverted_index.effects | 57 |
| abstract_inverted_index.feature | 84 |
| abstract_inverted_index.general | 80 |
| abstract_inverted_index.improve | 83 |
| abstract_inverted_index.labeled | 63, 120 |
| abstract_inverted_index.models, | 137 |
| abstract_inverted_index.present | 49 |
| abstract_inverted_index.propose | 66 |
| abstract_inverted_index.results | 117 |
| abstract_inverted_index.similar | 77 |
| abstract_inverted_index.smaller | 166 |
| abstract_inverted_index.various | 133 |
| abstract_inverted_index.approach | 70, 91 |
| abstract_inverted_index.classes. | 18 |
| abstract_inverted_index.corpora. | 142, 167 |
| abstract_inverted_index.datasets | 121 |
| abstract_inverted_index.entities | 12 |
| abstract_inverted_index.features | 95 |
| abstract_inverted_index.incurred | 159 |
| abstract_inverted_index.learning | 93 |
| abstract_inverted_index.mitigate | 55, 155 |
| abstract_inverted_index.proposed | 32 |
| abstract_inverted_index.training | 112 |
| abstract_inverted_index.datasets, | 139 |
| abstract_inverted_index.datasets. | 44, 64 |
| abstract_inverted_index.framework | 126 |
| abstract_inverted_index.introduce | 108 |
| abstract_inverted_index.languages | 26, 34 |
| abstract_inverted_index.unlabeled | 98, 151 |
| abstract_inverted_index.adaptation | 75, 125, 148 |
| abstract_inverted_index.approaches | 31 |
| abstract_inverted_index.extraction | 85 |
| abstract_inverted_index.predefined | 17 |
| abstract_inverted_index.relatively | 24 |
| abstract_inverted_index.variations | 110 |
| abstract_inverted_index.Recognition | 2 |
| abstract_inverted_index.Transformer | 136, 162 |
| abstract_inverted_index.adversarial | 74, 147 |
| abstract_inverted_index.classifying | 10 |
| abstract_inverted_index.identifying | 8 |
| abstract_inverted_index.large-scale | 14, 97, 150 |
| abstract_inverted_index.outperforms | 127 |
| abstract_inverted_index.performance | 157 |
| abstract_inverted_index.pre-trained | 164 |
| abstract_inverted_index.Experimental | 116 |
| abstract_inverted_index.combinations | 134 |
| abstract_inverted_index.non-adaptive | 130 |
| abstract_inverted_index.overfitting. | 115 |
| abstract_inverted_index.corresponding | 129 |
| abstract_inverted_index.generalization. | 89 |
| abstract_inverted_index.limited-resource | 25 |
| abstract_inverted_index.Transformer-based | 68 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |