一种高重复性和高准确性的主题自动分类方法
A high-reproducibility and high-accuracy method for automated topic classification
摘要：
人类的大部分知识都存在于由非结构化文本组成的大型数据库中。利用这些知识需要算法来提取和记录非结构化文本文档上的元数据。将主题转换为文档将使智能搜索、统计特征和有意义的分类成为可能。潜在狄利克雷分配（LDA）是主题分类的最新研究成果。在这里，我们进行了系统的理论和数值分析表明，目前的LDA优化技术往往产生的结果是不准确的推断最合适的模型参数。针对网络中的社区发现问题，提出了一种新的算法，该算法具有高重复性和高准确性，同时具有较高的计算效率。我们将其应用到一个大型的英文维基百科的文档集，并揭示其层次结构。我们的算法有望使“大数据”文本分析系统更加可靠。
英文摘要：
Much of human knowledge sits in large databases of unstructured text. Leveraging this knowledge requires algorithms that extract and record metadata on unstructured text documents. Assigning topics to documents will enable intelligent search, statistical characterization, and meaningful classification. Latent Dirichlet allocation (LDA) is the state-of-the-art in topic classification. Here, we perform a systematic theoretical and numerical analysis that demonstrates that current optimization techniques for LDA often yield results which are not accurate in inferring the most suitable model parameters. Adapting approaches for community detection in networks, we propose a new algorithm which displays high-reproducibility and high-accuracy, and also has high computational efficiency. We apply it to a large set of documents in the English Wikipedia and reveal its hierarchical structure. Our algorithm promises to make "big data" text analysis systems more reliable.
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。
