主题模型的网络方法
A network approach to topic models
摘要：
从非结构化文本中提取有用的信息是现代计算和科学的主要挑战之一。主题模型是一种流行的机器学习方法，它推断文档集合的潜在主题结构。尽管他们的成功-特别是其最广泛使用的变体称为潜在狄利克雷分配（LDA）-和社会学，历史学和语言学的众多应用，主题模型是众所周知的遭受严重的概念和实际问题，例如，缺乏理由的贝叶斯先验，与真实文本的统计特性的差异，以及无法正确选择主题的数量。在这里，我们获得了一个新的观点的问题，确定主题结构，将其与复杂网络中找到社区的问题。这是通过将文本语料库表示为文档和单词的二分网络来实现的。通过调整现有的社区检测方法-使用具有非参数先验的随机块模型（SBM）-我们获得了一个更通用和原则性的主题建模框架（例如，它自动检测主题的数量，并对单词和文档进行分层聚类）。人工语料和真实语料的分析表明，我们的SBM方法导致更好的主题模型比LDA的统计模型的选择。更重要的是，我们的工作展示了如何将社区检测和主题建模的方法正式联系起来，从而打开了这两个领域之间交叉施肥的可能性。
英文摘要：
One of the main computational and scientific challenges in the modern age is to extract useful information from unstructured texts. Topic models are one popular machine-learning approach which infers the latent topical structure of a collection of documents. Despite their success --- in particular of its most widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous applications in sociology, history, and linguistics, topic models are known to suffer from severe conceptual and practical problems, e.g. a lack of justification for the Bayesian priors, discrepancies with statistical properties of real texts, and the inability to properly choose the number of topics. Here we obtain a fresh view on the problem of identifying topical structures by relating it to the problem of finding communities in complex networks. This is achieved by representing text corpora as bipartite networks of documents and words. By adapting existing community-detection methods -- using a stochastic block model (SBM) with non-parametric priors -- we obtain a more versatile and principled framework for topic modeling (e.g., it automatically detects the number of topics and hierarchically clusters both the words and documents). The analysis of artificial and real corpora demonstrates that our SBM approach leads to better topic models than LDA in terms of statistical model selection. More importantly, our work shows how to formally relate methods from community detection and topic modeling, opening the possibility of cross-fertilization between these two fields.
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。
