Summary of Ner- Roberta: Fine-tuning Roberta For Named Entity Recognition (ner) Within Low-resource Languages, by Abdulhady Abas Abdullah et al.
NER- RoBERTa: Fine-Tuning RoBERTa for Named Entity Recognition (NER) within low-resource languages
by Abdulhady Abas Abdullah, Srwa Hasan Abdulla, Dalia Mohammad Toufiq, Halgurd S. Maghdid, Tarik A. Rashid, Pakshan F. Farho, Shadan Sh. Sabr, Akar H. Taher, Darya S. Hamad, Hadi Veisi, Aras T. Asaad
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a methodology for fine-tuning the pre-trained RoBERTa model for Kurdish Named Entity Recognition (KNER), addressing the limitation of limited datasets and challenging linguistic structures in Kurdish Natural Language Processing (KNLP). The approach involves creating a Kurdish corpus, designing a modified model architecture, and implementing training procedures. The authors evaluate their trained model using different tokenization methods and trained models, demonstrating improved performance with a fine-tuned RoBERTa model using SentencePiece tokenization, achieving a 12.8% improvement in F1-score compared to traditional models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve Kurdish Natural Language Processing (KNLP) by developing a better Named Entity Recognition (NER) system for the Kurdish language. Currently, KNER is a challenge due to limited datasets and unique linguistic structures. The authors create a new corpus and train a model using RoBERTa. They test their model with different methods and show that it works well, making it a useful tool for people who speak Kurdish. |
Keywords
» Artificial intelligence » F1 score » Fine tuning » Named entity recognition » Natural language processing » Ner » Tokenization
