论文标题
文本文档流的组织的上下文化
Contextualization for the Organization of Text Documents Streams
论文作者
论文摘要
研究界已经做出了巨大的努力，以解决提供信息检索方法组织文档的方法的问题。在本报告论文中，我们提供了一些实验，其中一些流分析方法探索文本文档的流。我们仅使用动态算法来探索，分析和组织文本文档的通量。该文档使用增量算法（如增量Textrank和IS-TFIDF）展示了具有文本文档流组织的开发架构的案例研究。这两种算法都是基于以下假设：与批处理算法相比，在较低维度不断发展的网络中，文本文档及其文档 - 期限矩阵的映射提供了更快的处理。使用此体系结构，并使用FastText嵌入来检索文档之间的相似性，我们将方法与大型文本数据集和聚类能力的地面真相评估进行了比较。所使用的数据集是路透社和Covid-19情绪。结果基于通量中文档之间的相似性和使用上述算法，在接近文档任务的相似性时，为相似性的情境化提供了新的视图。
There has been a significant effort by the research community to address the problem of providing methods to organize documentation with the help of information Retrieval methods. In this report paper, we present several experiments with some stream analysis methods to explore streams of text documents. We use only dynamic algorithms to explore, analyze, and organize the flux of text documents. This document shows a case study with developed architectures of a Text Document Stream Organization, using incremental algorithms like Incremental TextRank, and IS-TFIDF. Both these algorithms are based on the assumption that the mapping of text documents and their document-term matrix in lower-dimensional evolving networks provides faster processing when compared to batch algorithms. With this architecture, and by using FastText Embedding to retrieve similarity between documents, we compare methods with large text datasets and ground truth evaluation of clustering capacities. The datasets used were Reuters and COVID-19 emotions. The results provide a new view for the contextualization of similarity when approaching flux of documents organization tasks, based on the similarity between documents in the flux, and by using mentioned algorithms.
