改进的带聚类分配的K-means算法——应用于COVID-19数据
Modified K-means with Cluster Assignment -- Application to COVID-19 Data
AI总结：
本文提出一种改进的K-means文本提取模型，采用两聚类分配技术和向量平均阻尼，在COVID-19语料上取得良好效果。
摘要：
文本提取是一个高度主观的问题，它取决于所处理的数据集以及需要提取的摘要细节类型。从数据预处理到选择最优预测模型的所有步骤，都取决于所面临的问题和语料库。在本文中，我们描述了一个文本提取模型，其目标是提取与语义相关的指定词语信息，从而以简洁的格式获得关于该词语的所有相关且有意义的信息。该模型能够获得有意义的结果，并可增强通用的搜索模型或普通的聚类或主题建模算法。通过利用一种称为K-means模型的两聚类分配技术的新方法，我们改进了检索文本的本体结构。我们进一步应用向量平均阻尼技术来实现聚类的灵活移动。我们在最近的COVID-19语料库上的实验结果表明，基于主要关键词我们获得了良好的结果。
英文摘要：
Text extraction is a highly subjective problem which depends on the dataset that one is working on and the kind of summarization details that needs to be extracted out. All the steps ranging from preprocessing of the data, to the choice of an optimal model for predictions, depends on the problem and the corpus at hand. In this paper, we describe a text extraction model where the aim is to extract word specified information relating to the semantics such that we can get all related and meaningful information about that word in a succinct format. This model can obtain meaningful results and can augment ubiquitous search model or a normal clustering or topic modelling algorithms. By utilizing new technique called two cluster assignment technique with K-means model, we improved the ontology of the retrieved text. We further apply the vector average damping technique for flexible movement of clusters. Our experimental results on a recent corpus of Covid-19 shows that we obtain good results based on main keywords.
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。
