追踪复杂主题结构演化的分层Dirichlet过程及其在自闭症研究文献中的应用
Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
摘要：
在本文中，我们描述了一个新的框架发现的主题内容的数据语料库，并跟踪其复杂的结构变化的时间维度。与以前的工作相比，我们的模型没有对文档添加到语料库的速率施加先验，也没有采用马尔可夫假设，该假设过度限制了模型可以捕获的变化类型。我们的关键技术贡献是一个框架的基础上（i）离散化的时间到时代，（ii）时代明智的主题发现使用分层的Dirichlet过程为基础的模型，（iii）时间相似性图，允许建模复杂的主题变化：出现和消失，演变，分裂和合并。与自闭症谱系障碍（ASD）-一个越来越重要的研究课题，显着的社会和医疗保健的重要性的医学文献语料库上证明了所提出的框架的力量。除了收集的ASD文献语料库，我们将免费提供，我们的贡献还包括两个免费的在线工具，我们建立了ASD研究人员的辅助工具。这些可以用于语义上有意义的导航和搜索，以及从这个庞大且快速增长的文献语料库中发现知识。
英文摘要：
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, and splitting and merging. The power of the proposed framework is demonstrated on the medical literature corpus concerned with the autism spectrum disorder (ASD) - an increasingly important research subject of significant social and healthcare importance. In addition to the collected ASD literature corpus which we will make freely available, our contributions also include two free online tools we built as aids to ASD researchers. These can be used for semantically meaningful navigation and searching, as well as knowledge discovery from this large and rapidly growing corpus of literature.
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。
