基于掩码语言模型的极性分析新半监督技术
A New Semisupervised Technique for Polarity Analysis using Masked Language Models
AI总结：
本文提出利用掩码语言模型改进的潜在语义缩放方法，通过词向量预测种子词在上下文中的出现概率，提升文本分析中极性评分的准确性与一致性。
摘要：
我开发了一种新的潜在语义缩放（LSS）版本，采用词2vec作为掩码语言模型。与原有空间模型不同，该方法将词和文档的极性评分作为种子词在给定上下文中出现的概率进行预测。这些概率极性评分在文本分析中比空间极性模型产生的更准确、可解释且一致。我通过将概率模型和空间模型应用于中国日报关于新冠疫情中中国和其他国家在健康问题上的报道，展示了这些优势。结果表明，更先进的掩码语言模型将进一步改进半监督机器学习技术。
英文摘要：
I developed a new version of Latent Semantic Scaling (LSS) employing word2vec as a masked language model. Unlike original spatial models, it assigns polarity scores to words and documents as predicted probabilities of seed words to occur in given contexts. These probabilistic polarity scores are more accurate, interpretable and consistent than those spatial polarity models can produce in text analysis. I demonstrate these advantages by applying both probabilistic and spatial models to China Daily's coverage of China and other countries during the coronavirus disease (COVID) pandemic in terms of achievement in health issues. The result suggests that more advanced masked language models would further improve the semisupervised machine learning technique.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
