论文标题
基于BERT预培训技术的在线问答社区的标签建议
Tag Recommendation for Online Q&A Communities based on BERT Pre-Training Technique
论文作者
论文摘要
在线问答和开源社区使用标签和关键字来索引，分类和搜索特定内容。标签建议的最明显优势是正确的信息分类。在这项研究中，我们首次将BERT预培训技术用于在线问答和开源社区的标签建议任务中。我们对Freeecode数据集的评估表明，与深度学习和其他基线方法相比，所提出的称为Tagbert的方法更准确。此外，我们的模型通过解决先前研究的问题实现了高稳定性，在这种研究中增加了标签建议的数量可显着降低模型性能。
Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content. The most obvious advantage of tag recommendation is the correct classification of information. In this study, we used the BERT pre-training technique in tag recommendation task for online Q&A and open-source communities for the first time. Our evaluation on freecode datasets show that the proposed method, called TagBERT, is more accurate compared to deep learning and other baseline methods. Moreover, our model achieved a high stability by solving the problem of previous researches, where increasing the number of tag recommendations significantly reduced model performance.
