BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Abstract 動機：隨着生物醫學文獻數量的快速增長，生物醫學文本挖掘變得越來越重要。隨着自然語言處理（NLP）的進步，從生物醫學文獻中提取有價值的信息已在研究人員中廣受歡迎，而深度學習促進了有效的生物醫學文本挖掘模型的發展。但是，由於單詞分佈從普通領域的語料庫轉移到生物醫學的語料庫，直接將NLP的進步應用到生物醫學的文本挖掘常常會產生不令人滿意的結果。在本文中，我們研究了最近引入的預訓練語言模
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相關文章
- 1. BioBERT: a pre-trained biomedical language representation model for biomedical text mining
- 2. 《VideoBERT: A Joint Model for Video and Language Representation Learning》
- 3. Recognizing irregular entities in biomedical text via deep neural networks
- 4. Context-Aware U-Net for Biomedical Image Segmentation
- 5. 生物醫學命名實體識別（BioNER）研究進展
- 6. U-Net: Convolutional Networks for Biomedical Image Segmentation
- 7. 【論文筆記】Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
- 8. Universal Language Model Fine-tuning for Text Classification
- 9. Paper | U-Net: Convolutional Networks for Biomedical Image Segmentation
- 10. NLP pretrained model