Multi-Topic Categorization in a Low-Resource Ewe Language: A Modern Transformer Approach
Author:
Affiliation:
1. University of Electronic Science and Technology of China,School of Computer Science and Engineering,Chengdu,China
Funder
National Key Research and Development Program of China
National Science Foundation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9845797/9845820/09846372.pdf?arnumber=9846372
Reference24 articles.
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3. When Low Resource NLP Meets Unsupervised Language Model: Meta-Pretraining then Meta-Learning for Few-Shot Text Classification (Student Abstract)
4. Combining rule-based and statistical mechanisms for low-resource named entity recognition
5. Ve'rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement;alnajjar,2021
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1. Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language;Systems;2023-12-19
2. Efficient Adaptive Convolutional Model Based on Label Embedding for Text Classification Using Low Resource Languages;Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence;2023-04-23
3. Attention Based BiGRU-2DCNN with Hunger Game Search Technique for Low-Resource Document-Level Sentiment Classification;Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision;2023-01-13
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