BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Abstract 动机：随着生物医学文献数量的快速增长，生物医学文本挖掘变得越来越重要。随着自然语言处理（NLP）的进步，从生物医学文献中提取有价值的信息已在研究人员中广受欢迎，而深度学习促进了有效的生物医学文本挖掘模型的发展。但是，由于单词分布从普通领域的语料库转移到生物医学的语料库，直接将NLP的进步应用到生物医学的文本挖掘常常会产生不令人满意的结果。在本文中，我们研究了最近引入的预训练语言模
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相关文章
- 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. 生物医学命名实体识别（BioNER）研究进展
- 6. U-Net: Convolutional Networks for Biomedical Image Segmentation
- 7. 【论文笔记】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